Increasing dynamic range of a virtual production display

ABSTRACT

A processor performing postprocessing obtains an input image containing both bright and dark regions. The processor obtains a threshold between a first pixel value of the virtual production display and a second pixel value of the virtual production display. The processor modifies the region according to predetermined steps producing a pattern unlikely to occur within the input image, where the pattern corresponds to a difference between the original pixel value and the threshold. The processor can replace the region of the input image with the pattern to obtain a modified image. The virtual production display can present the modified image. A processor performing postprocessing detects the pattern within the modified image displayed on the virtual production display. The processor calculates the original pixel value of the region by reversing the predetermined steps. The processor replaces the pattern in the modified image with the original pixel value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Pat. Application SerialNumber 17/538,921 filed Nov. 30, 2021, which claims priority to the U.S.Provisional Pat. application Serial Number 63/283,927 filed Nov. 29,2021 both of which are incorporated herein by reference in theirentirety.

BACKGROUND

Virtual production is a technique of recording a movie on a soundstagewith a specially constructed display, e.g., an LED wall, and ceilingthat together form a virtual production set. The virtual production setenvelops the actors in a virtual display-like a giant TV screen-of anyconceivable environment that is displayed and lights the actors with thesame lighting profile that is illuminating the virtual objects seen onscreen. The background could be the dunes of an extraterrestrial planetor the interior of a lamplit Parisian restaurant. In the virtualproduction set, actors are lit with the correct natural lightingappropriate to the virtual world around them, in the studio and oncamera, because the screen, in addition to providing the imagery, alsoprovides the illumination.

A virtual production display is limited by the pixel value that it candisplay. Creating high-fidelity movies requires recording images havinga high dynamic range of pixel value.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed descriptions of implementations of the present invention willbe described and explained through the use of the accompanying drawings.

FIGS. 1A-1B show a virtual production set.

FIG. 2 shows a high-contrast image to be shown on the display.

FIG. 3 shows splitting of the image based on luminance.

FIG. 4 shows a system to record the first image and the second image.

FIG. 5A shows a system to calibrate the luminance of the display.

FIG. 5B shows fitting a function to mutually corresponding pixel valueregions.

FIG. 6 is a flowchart of a method to increase dynamic range of an imagerecorded from a display, according to one technique.

FIG. 7 shows a system to increase dynamic range of an image recordedfrom a virtual production display, according to one implementation.

FIG. 8A shows a standard dynamic range gamma function (“gammafunction”).

FIG. 8B shows a high dynamic range (HDR) function overlapping with thegamma function.

FIG. 9A shows a function which is the inverse of the gamma function.

FIG. 9B shows a function which is the inverse of the HDR function.

FIGS. 10A-10B show a flowchart of a method to increase dynamic range ofan image recorded from a display, according to another technique.

FIG. 11 shows an image containing pixel values exceeding the thresholdpixel value of the display.

FIG. 12 shows a pattern unlikely to occur in the image and indicatingtrue pixel value of the image, according to one embodiment.

FIG. 13 shows a pattern unlikely to occur in the image and indicatingtrue pixel value of the image, according to another implementation.

FIGS. 14A-14B show a flowchart of a method to increase dynamic range ofan image recorded from a display, according to another technique.

FIG. 15 shows a system to increase dynamic range of an image recordedfrom a virtual production display, according to another implementation.

FIG. 16 shows a correspondence between an input image and a recordedimage.

FIG. 17 shows a process to retrieve the original pixel value of theinput image using a map and a representation of the input image.

FIG. 18 shows a representation of the input image.

FIG. 19 shows a map between a pixel in the representation of the inputimage, and a pixel in a recorded image.

FIGS. 20A-20B show a flowchart of a method to increase dynamic range ofan image recorded from a display, according to another technique.

FIGS. 21A-21B show a flowchart of a method to calibrate an interactionbetween a display and a camera.

FIG. 22 illustrates an example visual content generation system as mightbe used to generate imagery in the form of still images and/or videosequences of images.

FIG. 23 is a block diagram that illustrates a computer system upon whichthe computer systems of the systems described herein and/or visualcontent generation system may be implemented.

The technologies described herein will become more apparent to thoseskilled in the art from studying the Detailed Description in conjunctionwith the drawings. Embodiments or implementations describing aspects ofthe invention are illustrated by way of example, and the same referencescan indicate similar elements. While the drawings depict variousimplementations for the purpose of illustration, those skilled in theart will recognize that alternative implementations can be employedwithout departing from the principles of the present technologies.Accordingly, while specific implementations are shown in the drawings,the technology is amenable to various modifications.

DETAILED DESCRIPTION

Disclosed here are various techniques to increase dynamic range of animage recorded from a display. Increasing dynamic range can includemaking the image recorded from the display darker, brighter, and/or morecolorful. In one technique, a processor performing preprocessing splitsan input image containing both bright and dark regions into two images,image A containing bright regions, and image B containing dark regions.The virtual production display presents image A and image B inalternating fashion. Camera A and camera B are synchronized with thedisplay so that camera A records image A, and camera B records image B.In postprocessing, a processor obtains the recorded images A and B. Theprocessor increases the pixel value of the recorded image A to obtainimage A with increased pixel value. To increase the pixel value of therecorded image A, the processor can apply a pixel value increasingfunction such as an inverse of a hybrid log-gamma function, an inverseof a log encoding function, an inverse of a perceptual quantizerfunction, and/or a calibration function. Finally, the processorincreases pixel value of the image recorded from the display bycombining the first recorded image with increased pixel value and thesecond recorded image.

In another technique, a processor performing preprocessing obtains aninput image containing both bright and dark regions. The processorperforming preprocessing obtains a first pixel value of the virtualproduction display and a second pixel value of the virtual productiondisplay, where the first pixel value and the second pixel value indicatea dynamic range of the display. The processor determines a desired pixelvalue range that exceeds the second pixel value of the virtualproduction display. The desired pixel value range can correspond to apixel value that the camera can record and that exceeds the second pixelvalue of the virtual production display. In another example, the desiredpixel value range can correspond to a pixel value that an observer ofthe image can perceive and that exceeds the second pixel value of thevirtual production display. In a third example, the desired pixel valuerange corresponds to a second pixel value that can be recorded in theimage. The processor can obtain a threshold between the first pixelvalue of the virtual production display and the second pixel value ofthe virtual production display. The processor can obtain a functionmapping the desired pixel value range to a range between the thresholdand the second pixel value of the virtual production display. Theprocessor can apply the first function to the input image prior todisplaying the input image on the virtual production display. Uponapplying the function, the virtual production display can present theimage. Upon recording the presented image, a processor performingpostprocessing can determine a region within the recorded image having apixel value within the range between the threshold and the second pixelvalue of the virtual production display. The processor can increasedynamic range of the image recorded from the virtual production displayby applying an inverse of the function to the pixel value of the region,where the inverse of the first function increases the pixel value of theregion.

In a third technique, a processor performing postprocessing obtains aninput image containing both bright and dark regions. The processor canobtain a threshold between a first pixel value of the virtual productiondisplay and a second pixel value of the virtual production display. Upondetecting a region of the input image having an original pixel valueabove the threshold, the processor can modify the region according topredetermined steps producing a pattern unlikely to occur within theinput image, where the pattern corresponds to a difference between theoriginal pixel value and the threshold. The processor can replace theregion of the input image with the pattern to obtain a modified image.The virtual production display can present the modified image. Aprocessor performing postprocessing can detect the pattern within themodified image displayed on the virtual production display. Theprocessor can calculate the original pixel value of the region byreversing the predetermined steps. The processor can replace the patternin the modified image with the original pixel value.

In a fourth technique, a processor performing postprocessing obtains aninput image containing both bright and dark regions. The processor canobtain a threshold between a first pixel value of the virtual productiondisplay and a second pixel value of the virtual production display. Upondetecting a region of the input image having an original pixel valueabove the threshold, the processor can create a data structure includinga location of the region in the input image and an original pixel valueof the region. The data structure occupies less memory than the inputimage. The virtual production display can present the input imageincluding the region of the image having the original pixel value abovethe threshold. The processor can send the data structure to a processorassociated with the camera recording the presented image. The processorassociated with the camera can perform postprocessing. Once the camerarecords the presented image, the processor performing postprocessing canobtain the data structure in the recorded image, and increase dynamicrange of the recorded image by modifying the recorded image based on therepresentation of the input image to obtain an output image. The pixelvalue of the output image can closely approximate, or exactly match, thepixel value of the input image.

A processor can calibrate the camera. The processor can present an inputimage on the display to obtain a presented image. The camera can recordthe presented image to obtain a recorded image, where the camera isarbitrarily positioned relative to the display. The processor can obtainthe input image via a channel different from the display. The processorcan obtain an indication of a display region associated with thedisplay. The processor can determine an input image region correspondingto the display region, and a recorded image region corresponding to thedisplay region. The processor can obtain a first pixel value associatedwith the input image region and a second pixel value associated with therecorded image region, where a pixel value is a set of numbers defininghue, saturation and intensity. The processor can determine a mappingbetween the first pixel value and the second pixel value, where applyingthe mapping to the second pixel value substantially produces the firstpixel value. The processor can store an identifier associated with therecorded image region and the mapping.

The description and associated drawings are illustrative examples andare not to be construed as limiting. This disclosure provides certaindetails for a thorough understanding and enabling description of theseexamples. One skilled in the relevant technology will understand,however, that the invention can be practiced without many of thesedetails. Likewise, one skilled in the relevant technology willunderstand that the invention can include well-known structures orfeatures that are not shown or described in detail, to avoidunnecessarily obscuring the descriptions of examples.

I. Increasing Dynamic Range of a Virtual Production Display Using ImageSplitting

FIGS. 1A-1B show a virtual production set. The virtual production set100 includes a virtual production display (“display”) 110 which cansurround a stage 120. The display 110 can include multiple screens 110A,110B, 110C. One or more of the screens 110A, 110B, 110C can be curved.The size of each screen 110A, 110B, 110C can correspond to a size of awall and exceed 6 feet in height and 3 feet in width. The screen can usevarious display technologies such as LCD, LED, OLED, a projector, etc.

The stage 120 is sufficiently large to include multiple actors 130 andprops 140, 150, 160. The stage 120 can seamlessly integrate with thescreens 110A, 110B, 110C presenting images 115A, 115B, 115C,respectively. For example, the stage 120 can include props, such asrocks 150 and sand 160, that mimic the appearance of rocks 170 and sand180 that appear on the display 110.

The display 110 illuminates the stage 120, actors 130, and props 140,150, 160. Thus, the lighting of the environment and the lighting of theactors 130 and props 140, 150, 160 matches. In particular, highlyreflective surfaces, such as metallic surfaces, properly reflect theenvironment. In addition to the display 110 illumination, additionallights 190 can illuminate the stage 120.

The display 110 needs to update the images 115A, 115B, 115C to reflectevents on the stage 120 such as motion of the actors 130, parallax tocorrectly create a sense of depth, interaction between the actors 130and the images 115A-C, etc. In other words, the display 110 needs torender in real time. A rendering engine 125, such as Unreal Engine orGazebo, running on a processor 135 can render the images 115A-C in realtime in response to events on the stage 120. The rendering engine 125can communicate with a camera 105 using a wired or a wireless network.

The camera 105 can record the stage 120 including images presented onthe display 110, actors 130, and props 140, 150, 160. The camera 105 andthe processor 135 can each be coupled to a wireless transceiver 165,145, respectively, through which the rendering engine 125 can track thecamera movement, and through which the processor and the camera cancommunicate.

FIG. 2 shows a high-contrast image 200 to be shown on the display 110.The display 110 can have a limited range of luminance. Consequently, thedisplay 110 cannot faithfully display the high dynamic range of theimage 200. The high dynamic range can include the low luminance regions210 and the high luminance regions 220. To display the image 200, thedisplay 110 can clip the high luminance regions 220, and display theimage 230. In image 230, the high luminance region 220 has been clippedto match a lower luminance region 240.

Going back to FIGS. 1A-1B, as described in this application, theprocessor 135 can apply various techniques to increase dynamic range ofan input image 175, prior to presenting the input image on the display110. After the processor 135 applies the technique, a processor 155associated with the camera 105 in FIG. 1B can apply the inverse of thetechnique to retrieve the original luminance of the images 115A-C. Inother words, the processor 135 and the processor 155 need to agree whichtechnique to apply to the images 115A-C.

To achieve the agreement, in one implementation, the processors 135, 155can exchange information about the technique to apply using a wirelesstransceiver 145, 165 associated with each processor. In anotherimplementation, the processors 135, 155 can receive an input from a userindicating the technique to be used. The user can specify the inputthrough a user interface such as a graphical user interface, an audiouser interface, or a hardware user interface. The hardware userinterface can include a dial or a switch. In a third implementation, aregion of the images 115A-C can include a signal to indicate to theprocessor 135 the technique being used. The signal can be encoded usinga watermark.

The processor 135 can apply one or more of the dynamic rangemodification techniques described in this invention to a single image115. For example, the processor 135 can apply technique A and techniqueB. The processor 135 communicates the applied techniques to theprocessor 155. Consequently, after the camera 105 records the displayedimage, the processor 155 applies the inverse of technique B and then theinverse of technique A to the displayed image, to obtain the originalimage 115. The processor 155 applies the techniques A and B in reverseorder of processor 135.

FIG. 3 shows splitting of the image 200 based on luminance. To increasedynamic range of an image recorded from a virtual production display, aprocessor can split the image 200, based on a luminance threshold(“threshold”) 320, into a first image 300, and a second image 310, wherethe first image includes brighter portions of the image 200 than thesecond image 310. For example, the first image 300 can include pixelsfrom the image 200 that are brighter than the luminance threshold 320,while the second image 310 can include pixels from the image 200 thatare equal to or darker than the luminance threshold 320. The display 110can show the first image 300 and the second image 310 in an alternatingfashion.

The luminance threshold 320 can correspond to the maximum luminance ofthe display 110 in FIG. 1A. For example, the luminance threshold 320 canbe the same as the maximum luminance of the display 110, or a fractionof the maximum luminance of the display.

Alternatively, the luminance threshold 320 can correspond to theluminance range contained in the image 200. For example, the luminancethreshold 320 can be a fraction of the maximum luminance of the image,such as 50%.

The regions 330, 340 of the images 300, 310, respectively, represent theportion of the image 200 that is not included in the images 300, 310,respectively. The regions 330, 340 can include a predetermined valueindicating that the portion of the images 300, 310 is functionallyequivalent to a green screen, that is, a region that needs to bereplaced in postproduction. The predetermined value can be black, suchas 0.

FIG. 4 shows a system to record the first image and the second image.The system 400 includes two cameras 410, 420 and the display 430. Thedisplay 430 can be an all-around display, as shown in FIG. 1A.

The cameras 410, 420 can be synchronized to the display 430, such thatwhen the display is presenting the first image 440 the camera 410 isrecording, and the camera 420 is not. Similarly, when the display ispresenting the second image 450, the camera 420 is recording, and thecamera 410 is not. Consequently, the camera 410 can consistently recordthe first image 440 containing brighter pixels to obtain recorded image445, while the camera 420 can consistently record the second image 450containing darker pixels to obtain recorded image 455. The two imagescan be combined in postprocessing, after being recorded, to re-create animage 480 having a high dynamic range.

The benefit of alternating the first image 440 and the second image 450is at least twofold. First, presenting only the bright portions of theimage or only the dark portions of the image enables the display todedicate the full range of the display’s luminance to just the brightportions of the image or just the dark portions of the image. Second,recording only the bright portions of the image or recording only thedark portions of the image enables independent postprocessing of thebright portions and the dark portions of the image.

To explain the first benefit, let us assume that the display 430 canshow pixels having a luminance between 0 and 100, while the image 200 inFIG. 2 includes pixels having a luminance between 0 and 200. The firstimage 440 can contain pixels having the luminance between 101 and 200,while the second image 450 can contain pixels having the luminancebetween 0 and 100. By displaying the two images 440 and 450independently, the processor enables the display 430 to use the fullluminance range 0 to 100 to display the dark pixels of image 450, andalso enables the display to use the full luminance range 0 to 100 todisplay the bright pixels of image 440. Without splitting the image 200into images 440 and 450, the display 430 would have to use the luminancerange 0 to 100 to display the full range of luminance 0 to 200.Consequently, different luminance values in the image 200 would have tobe mapped to the same luminance value of the display 110.

Regarding the second benefit, a processor receiving the bright image 440recorded by the camera 410 can apply a function to the recording of thebright image 440 to further increase the luminance of the bright image440. Specifically, let us assume, as explained above, that the pixelshaving luminance between 101 and 200 need to be presented on the display430 having luminance range of 0-100. To map the values 101-200 to 0-100,a first processor, in a preprocessing stage, can apply a function 460 tothe first image 440. The display 430 can display the resulting image.The camera 410 can record the resulting image. A second processor, in apostprocessing stage, can apply an inverse function 470 to the recordedimage 445. The inverse function 470 is an inverse of the function 460.The inverse function 470 can take the values in the range 0-100, andconvert them to the values in the range 101-200, thereby closelyapproximating or even re-creating the first image 440. Finally, thesecond processor can combine the re-created first image 440 and therecorded second image 450 to obtain the final image 480. The final image480 can exactly match or closely correspond to the input image 200 inFIG. 2 .

Optionally, the second image 450 can also go through preprocessing andpostprocessing steps. Let us assume that the display 430 can show pixelshaving a luminance between 0 and 100, while the input image 200 includespixels having a luminance between 0 and 400. The first image 440 cancontain pixels having the luminance between 201 and 400, while thesecond image 450 can contain pixels having the luminance between 0 and200. To map the values 0-200 to 0-100, the first processor, in apreprocessing stage, can apply a function 490 to the second image 450.The display 430 can display the resulting image. The camera 420 canrecord the resulting image. The second processor, in a postprocessingstage, can apply an inverse function 405 to the recorded image 455. Theinverse function 405 is an inverse of the function 490. The inversefunction 405 can take the values in the range 0-100, and convert them tothe values in the range 0-200, thereby closely approximating or evenre-creating the second image 450. Finally, the second processor cancombine the re-created first image 440 and the re-created second image450 to obtain the final image 480. The final image 480 can match theinput image 200 in FIG. 2 .

FIG. 5A shows a system to calibrate the luminance of the display. Thedisplay 500 can be a part of the virtual production display 110 in FIG.1A, or can be a standalone display such as a computer monitor, a TV, atablet display, a mobile device display, a wearable display, etc. Tocalibrate the luminance of the display 500, a processor can obtain oneor more images 510, 520, 530 and present the image 510, 520, 530 on thedisplay 500. The processor can determine the luminance of a region 510A,520A, 530A in the image 510, 520, 530, and the corresponding luminanceof a region 500A, 500C, 500E on the display 500, when the display ispresenting the image 510, 520, 530, respectively. The region 510A, 520A,530A can correspond to a single pixel or to a group of pixels in theimage 510, 520, 530. Each image 510, 520, 530 can have multiple regions510A, 510B, 520A, 520B, 530A, 530B (only two labeled in each image forbrevity), and multiple corresponding regions 500A, 500B, 500C, 500D,500E, 500F on the display. The multiple regions 510A, 510B of a singleimage 510 cover the whole image 510. The multiple regions 510A, 510B canoverlap.

The processor can determine a difference between the luminance of theregion 510A, 520A, 530A in the image 510, 520, 530, and the luminance ofthe region 500A on the display 500. Based on the difference, theprocessor can determine how the display 500 modifies the luminancecontained in the image 510, 520, 530. To determine the modification ofthe luminance, the processor can fit a function 540 through the obtainedluminance values, as explained below. The function 540 can take as inputluminance of the displayed image, and produce as output the luminance ofthe input image 510, 520, 530.

In another implementation, the processor can determine thecorrespondence between the luminance of the regions 510A, 510B, 520A,520B, 530A, 530B and the luminance of the regions 500A, 500B, 500C,500D, 500E, 500F corresponding to the regions in the recorded image550A, 550B, 550C. In this implementation, the function 540 can take asinput luminance of the recorded image 550A, 550B, 550C and produce asoutput image 545, 555, 565. The luminance of the output image 545, 555,565 can exactly match or closely approximate the luminance of the inputimage 510, 520, 530, respectively.

The function 540 can be used in conjunction with one or more of theluminance modification techniques described in this invention in asingle image 115. For example, the processor 135 in FIG. 1B caninitially apply image splitting and subsequently the calibrationfunction to a single image. After the camera 105 in FIGS. 1A-1B recordsthe displayed image, the processor 155 in FIG. 1B can initially applythe inverse of the calibration function, and subsequently reverse theimage splitting to obtain the original image 115.

The calibration images 510, 520, 530 can contain varying patterns usedin calibrating the display 500. For example, the calibration image 510can contain a gradient from a minimum to a maximum brightness. Thecalibration image 520 can include low luminance values, while thecalibration image 530 can include high luminance values.

The display 500 can be calibrated as a single unit, or can be split intotwo or more tiles 575, 585 which can be calibrated separately. If thetiles are calibrated separately, each tile can have a correspondingfunction 540.

FIG. 5B shows fitting a function to mutually corresponding luminanceregions. The X-axis 560 represents the luminance of a region 500A-F ofthe displayed image, while the Y-axis 570 represents the luminance of aregion 510A, 510B, 520A, 520B, 530A, 530B of the input image. In anotherimplementation, where the processor determines the correspondencebetween the luminance of the regions 510A, 510B, 520A, 520B, 530A, 530Band the luminance of the regions in the recorded image 550A, 550B, 550C,the X-axis 560 represents the luminance of a region in the recordedimage 550A, 550B, 550C.

A processor can fit the function, e.g., calibration function, 540through the points 580, 590, 505, 515, 525, 535 (only six labeled forbrevity). The fit of the function can be estimated using metrics, suchas least-squares distance between the function and the points 580, 590.

FIG. 6 is a flowchart of a method to increase luminance of an imagerecorded from a display, according to one technique. In step 600, ahardware or software processor executing instructions described in thisapplication can obtain the image to display on a display, such as avirtual production display, a computer monitor, a TV, a display of amobile device, a display of a tablet, a display of a wearable device,etc.

In step 610, the processor can obtain a splitting criterion, where thesplitting criterion indicates how to separate the image. In oneimplementation, to obtain the splitting criterion the processor cancalibrate the display by obtaining a pixel value threshold associatedwith the display. The pixel value threshold can indicate a pixel valuebetween a minimum pixel value that can be presented on the display in amaximum pixel value that can be presented in a display. The processorcan obtain the splitting criterion indicating to split the input imagebased on the pixel value threshold. In another implementation, to obtainthe splitting criterion, the processor can obtain an indication to splitthe input image based on a region.

In step 620, the processor can split the image into a first image and asecond image based on the splitting criterion. Combining the first imageand the second image produces the image. The processor can replaceportions of the first image not included in the image by a predeterminedvalue, and replace portions of the second image not included in theimage by the predetermined value. The predetermined value can be 0.

In one implementation, to split the image into the first image and thesecond image, the processor can obtain the pixel value threshold, andmultiple regions associated with the image. A region can be a pixel or agroup of pixels in the image. The processor can obtain a pixel value ofthe region among the multiple regions. If the region is a single pixel,the pixel value of the region is the pixel value of the pixel. If theregion contains multiple pixels, the pixel value of the region can be afunction of pixel value of each pixel contained in the region. Thefunction can be an average or can be a weighted function. The weightedfunction can add more weight to the pixel value of pixels in the middleof the region and less weight to the pixel value of the pixels on theperiphery of the region. The processor can determine whether the pixelvalue of the region is above the pixel value threshold. Upon determiningthat the pixel value of the region is above the pixel value threshold,the processor can add the region to the first image, thereby obtainingthe first image including brighter portions of the input image than thesecond image.

In another implementation, after obtaining an indication to split theimage based on the region, the processor can split the input image intothe first image and the second image including assigning the region tothe first image and a remainder of the input image to the second image.

In step 630, the processor can present the first image and the secondimage on the display in an alternating fashion. In step 640, theprocessor can record the first image by a first camera to obtain a firstrecorded image, and can record the second image by a second camera toobtain a second recorded image. The first camera and the second cameracan be the same camera. The first camera can be synchronized to thedisplay to record the first image, and the second camera can besynchronized to the virtual production display to record the secondimage. In other words, the first camera can be synchronized to recordwhile the first image is displayed, and to not record while the secondimage is displayed. The second camera can be synchronized to recordwhile the second image is displayed, and to not record while the firstimage is displayed.

In step 650, the processor can increase the dynamic range of the imagerecorded from the virtual production display by combining the firstrecorded image and the second recorded image.

The processor can increase the luminance of the first recorded image toobtain a first recorded image with increased luminance. To increase theluminance, the processor can obtain a luminance of a region amongmultiple regions associated with the first image. The processor canincrease the luminance of the first recorded image by applying afunction to the luminance of the region, where the function receives theluminance of the region as input, and produces an output increasing theluminance of the region.

The function can include an inverse of a hybrid log-gamma function, aninverse of a log encoding function, an inverse of a perceptual quantizerfunction, and/or a calibration function. The processor can combine oneor more functions and apply one function after the other. To increasethe luminance, the processor applies the functions in reverse of theorder in which the processor applies the functions prior to presentingthe image on the display.

Prior to presenting the first image, the processor can decreaseluminance of the first image by applying the function to the firstimage. The processor can increase luminance of the first recorded imageby obtaining a luminance of a region among multiple regions associatedwith the first image, and by applying an inverse of the function to theluminance of the region. The inverse of the function receives theluminance of the region as input, and produces an output increasing theluminance of the region.

The processor can calibrate the display by determining a calibrationfunction. To determine the calibration function, as explained in FIGS.5A-5B, the processor can obtain a first luminance of a display region,and a second luminance of an image region corresponding to the displayregion. The display region presents the image region. The processor candetermine a difference between the first luminance and the secondluminance. As explained in FIGS. 5A-5B, there can be multiple firstluminance values and multiple corresponding second luminance values,whose relationships are approximated by the calibration function 540 inFIG. 5B. Based on the difference between the first luminance and thesecond luminance, the processor can obtain a function 540 correlatingthe first luminance and the second luminance. The processor candetermine a region in the first recorded image corresponding to thevirtual production display region. Finally, the processor can increasethe luminance of the first recorded image by applying the function tothe region in the first recorded image corresponding to the virtualproduction display region.

To determine which technique to apply prior to displaying the image andafter recording the displayed image, respectively, the processors 135,155 in FIG. 1B can synchronize. The processor 135 can performpreprocessing, namely, modifying the image prior to the display, and theprocessor 155 can perform postprocessing, namely, modifying the imageafter the image is recorded. The processors 135, 155 can obtain an inputindicating a technique among multiple techniques, where the technique isconfigured to increase dynamic range of an image recorded from a virtualproduction display. The processors 135, 155 can receive an input througha hardware, such as a dial or a switch. The processors 135, 155 canwirelessly communicate with each other. The processor 135 can encode anindication of the technique used in the image, and the processor 155 candecode the indication of the technique from the recorded image. Theprocessors 135, 155 can be the same processor, or can be differentprocessors.

The processor 135 can apply a first portion of the technique to theimage prior to displaying the image on the virtual production display.After applying the technique, the processor 135 can send a signal to theprocessor 155 indicating the technique, as described above. Theprocessor 155 can apply a second portion of the technique to an imagerecorded from the virtual production display. The first portion of thetechnique and the second portion of the technique can be inverses ofeach other.

The virtual production display can contain sources of light that areexternal to the display, such as a lamp on the virtual production set.The processor can identify the external sources of light and can providea special treatment to the external sources of light. In oneimplementation, the processor can determine a source of light externalto the display. The processor can adjust intensity of the source oflight by a factor corresponding to a number of images into which theinput image is split. In another implementation, the processor candetermine a region in the first recorded image and the second recordedimage corresponding to a source of light external to the display. Theprocessor can ensure the region is not combined twice when combining thefirst recorded image and the second recorded image.

II. Increasing Dynamic Range of a Virtual Production Display Using aReversable Dynamic Range Mapping Operation

FIG. 7 shows a system to increase dynamic range of an image recordedfrom a virtual production display, according to one implementation. Aninput image 700 can include a region 710 of high pixel value, and aregion 720 of low pixel value. The display 730 has a limited range ofpixel value, such as between 0 and 100, where 0 can be the first pixelvalue of the display, and 100 can be the second pixel value of thedisplay. The second pixel value can be higher than the first pixelvalue. The first pixel value can be the minimum pixel value, while thesecond pixel value can be the maximum pixel value. The region 710 ofhigh pixel value can have pixel value in the range 101-200, while theregion 720 of low pixel value can have pixel value in the range 0-100.

Generally, to enable the display 730 to present the region 710 of highpixel value, the image 700 includes pixel values to which a function 800in FIG. 8A has been applied. The function 800 maps the pixel valuesabove the second pixel value of the display into the pixel value rangeof the display, e.g., between 0 and 100. However, the function 800 maystill clip the high pixel values, as explained in FIG. 8A. Instead ofapplying the function 800 that clips the high pixel values, a processor705, performing a preprocessing step, can apply a high dynamic range(HDR) function 740 that maps the pixel value range contained in theimage, e.g., 0-200, to distinct values in the display 730 pixel valuerange, e.g., 0-100. Consequently, the function 740 does not clip thehigh pixel values in the image 700.

Upon applying the function 740, the display 730 can display the modifiedimage 750, in which the pixel value range is within the pixel valuerange of the display 730. For example, the pixel value range of themodified image 750 is within 0-100.

A camera 760 can record the modified image 750. A processor 715performing a postprocessing step can receive the recorded modified image770 having a pixel value range corresponding to the pixel value range ofthe display 730, e.g., 0-100. The processor can apply the inversefunction 780 to obtain the output image 790 containing the originalpixel value of the image 700. The inverse function 780 is the inverse ofthe new HDR function 740.

In one embodiment, the image 700 can contain pixel values to whichfunction 800 has not been applied. In that case, the processor 705performing the preprocessing step applies the function 740 to map therange of the pixel value to 0-100, and the processor 715 performing thepostprocessing step applies the inverse function 780 to map pixel value0-100 to the image pixel value 0-200.

In another implementation, the image 700 can contain pixel values towhich function 800 has been applied. In that case, the processor 705 canapply an inverse of the function 800 to obtain pixel values in the range0-200, and subsequently apply the function 740 to map pixel values inthe range 0-200 to the range 0-100. The processor 715 performing thepostprocessing step applies the inverse function 780 to the recordedmodified image 770 to obtain the output image 790 containing the pixelvalues 0-200. The output image 790 can closely approximate, or evenexactly match, the input image 700.

FIG. 8A shows a standard dynamic range gamma function (“gamma function”)800. The X-axis 810 represents the pixel value of the input image 700 inFIG. 7 , while the Y-axis 820 represents the pixel value of the display730 in FIG. 7 . Value 840 represents the maximum desired pixel value.The maximum desired pixel value 840 can be the maximum pixel valuecontained in the input image 700, the maximum pixel value that thecamera 760 can record, and/or the maximum pixel value that an observerof the image can perceive. Value 850 represents the maximum pixel valuethe display 730 can present. Value 850 is greater than value 840. Value845 is the minimum desired pixel value, while value 855 is the minimumpixel value the display 730 can present. Values 845 and 855 can both be0.

The maximum pixel value the display 730 can present and the minimumpixel value the display can present can vary depending on what else ispresented on display, as described in this application. Specifically,the display can be limited by the amount of power that it can draw, andthe maximum pixel value can vary based on how much of the presentedimage has high luminance.

The gamma function 800 maps the larger range from value 845 to value 840into the range from value 855 to value 850. As can be seen in FIG. 8A,past a certain value 860, all values in the range 870 are clipped. Inother words, all values in the range 870 map to the same maximum pixelvalue 850. Once the pixel value is clipped, and the clipped pixel valueis presented on the display 730, the camera 760 and the processor 715 inFIG. 7 cannot retrieve the original pixel values contained in the inputimage 700.

FIG. 8B shows an HDR function 740 overlapping with the gamma function800. The HDR function 740 can depend on properties of the display 730,such as the minimum pixel value and the maximum pixel value, and theproperties of the desired pixel value.

A processor can obtain a threshold pixel value (“threshold”) 880 betweenthe first pixel value of the virtual production display and the secondpixel value of the virtual production display, where the first pixelvalue can be the minimum pixel value that the virtual production displaycan present, and the second pixel value can be the maximum pixel valuethat the virtual production display can present. The threshold can behigher than the average of the second pixel value 850 and the firstpixel value 855. For example, the threshold 880 can be 0.8 times thesecond pixel value 850 plus 0.2 times the first pixel value 855.

Up until the threshold 880, the HDR function 740 can match the gammafunction 800; however, at the threshold 880, the HDR function 740 candiverge from the gamma function 800 by mapping the desired pixel valueto a lower pixel value of the display 730 than the gamma function 800.As a result, the pixel values in the range 890, including the range 870,are mapped to the distinct pixel value of the display 730, and are notclipped. Consequently, once the camera 760 records the image processedusing the HDR function 740, the processor 715 in FIG. 7 can extract theoriginal pixel values contained in the input image 700, as explained inFIG. 5B.

At the pixel value 885, the gamma function 800 and the HDR function 740produce the same output value, namely, the threshold 880. In addition,the gamma function 800 and the HDR function 740 have the same firstderivative at the pixel value 885.

FIG. 9A shows a function 900 which is the inverse of the gamma function800. The X-axis 910 represents the pixel value of the display 730 inFIG. 7 , while the Y-axis 920 represents the pixel value of the outputimage 790 in FIG. 7 . All pixel values of the display 730 reaching orexceeding value 930 can correspond to any value in the range 935. Thevalue 930 can correspond to the maximum pixel value of the display 730in FIG. 7 . Consequently, applying the function 900 cannot accuratelyre-create the output image 790, so that the pixel value of the outputimage 790 matches the pixel value of the input image 700 in FIG. 7 .

FIG. 9B shows a function 780 which is the inverse of the HDR function740 in FIG. 8B. A processor can obtain a threshold pixel value(“threshold”) 940 between the minimum pixel value 955 of the virtualproduction display 730 in FIG. 7 and the maximum pixel value 950 of thevirtual production display. The threshold pixel value 940 can be higherthan the average of the maximum pixel value 950 and the minimum pixelvalue 955.

Up until the threshold 940, the function 780 can match the function 900.At the threshold 940, the function 780 can diverge from the function 900by mapping the output pixel value to a higher pixel value in the outputimage 790 in FIG. 7 than the function 900. As a result, the pixel valuesin the range 935 are mapped to a distinct pixel value in the outputimage 790, and are not clipped. Consequently, once the camera 760 inFIG. 7 records the image processed using the HDR function 740, theprocessor 715 in FIG. 7 can extract the original pixel values containedin the input image 700.

At the threshold 940, the function 900 and the function 780 produce thesame output value 960. In addition, the function 900 and the function780 have the same first derivative at threshold 940.

FIGS. 10A-10B show a flowchart of a method to increase dynamic range ofan image recorded from a display, according to another technique. Instep 1000, a hardware or software processor executing instructionsdescribed in this application can obtain the image to display on adisplay, such as a virtual production display, a computer monitor, a TV,a display of a mobile device, a display of a tablet, a display of awearable device, etc.

In step 1010, the processor can obtain a profile associated with thevirtual production display, where the profile associated with displayindicates a region of the display and a range of pixel values that theregion of the display can present. A pixel value is a set of numbersdefining hue, saturation and intensity. For example, to obtain theprofile, the processor can obtain a first pixel value of the virtualproduction display and a second pixel value of the virtual productiondisplay. The second pixel value can be higher than the first pixelvalue. The first pixel value can be the minimum pixel value the displaycan present, while the second pixel value can be the maximum pixel valuethe display can present.

In step 1020, the processor can determine a desired pixel value rangethat exceeds the second pixel value of the virtual production display.In one implementation, the desired pixel value range can correspond to apixel value that the camera can record and that exceeds the profile,e.g. the dynamic range, of the virtual production display. In anotherimplementation, the desired pixel value range can correspond to a pixelvalue that an observer of the image can perceive and that exceeds thedynamic range of the virtual production display. In a thirdimplementation, the desired pixel value range corresponds to a maximumpixel value that can be recorded in the image and that exceeds dynamicrange of the virtual production display. The dynamic range of thevirtual production display is between minimum and maximum pixel value ofthe virtual production display. The dynamic range can be lower thanmaximum pixel value due to power constraints or safety reasons. Forexample, the virtual production display can be powered by batteries, inwhich case, the power to the virtual production display is limited andoperating the virtual production display at maximum pixel value isundesirable.

In step 1030, the processor can obtain a threshold between the firstpixel value of the virtual production display and the second pixel valueof the virtual production display. The first pixel value and the secondpixel value indicate a range within the profile associated with thevirtual production display. The first pixel value can be the minimumpixel value of the display, while the second pixel value can be themaximum pixel value of the display. The threshold can be higher than theaverage of the first pixel value and the second pixel value. Forexample, the threshold can be a weighted average of the first pixelvalue and the second pixel value, where the second pixel value has aweight higher than 0.5, and the first pixel value has a weight of (1 -the weight of the second pixel value). In another example, the thresholdcan be equal to the second pixel value of the display. In a thirdexample, the threshold can be 80% of the second pixel value.

In step 1040, the processor can obtain a first function mapping thedesired pixel value range to a range between the threshold and thesecond pixel value of the virtual production display. The processor canobtain the first function from a lookup table, or the processor cancalculate the first function based on the brightness of the image or thesequences explained below. In one implementation, the first function canvary for each image presented on the display. For example, for a darkerimage, the first function can map dark pixels in the input image tobrighter values to be displayed on the display, similar to function 800in FIG. 8A. For a lighter image, the first function can map brightpixels in the input image to darker values to be displayed in thevirtual production display, to be able to accommodate pixels in theimage that are brighter than the second pixel value of the display.

In another implementation, the first function can vary based on asequence of images, such as a sequence in a movie. For example, a darksequence can be recorded in a cave, while a bright sequence can berecorded in intense natural light. For the bright sequence, the firstfunction can map dark pixels in the input image to brighter values to bedisplayed on the display. For the darker sequence, the first functioncan map bright pixels in the input image to darker values to bedisplayed in the virtual production display, to be able to accommodatepixels in the image that are brighter than the second pixel value of thedisplay. In this implementation, the same first function can be appliedto all the images in the sequence. A processor performing thepreprocessing of the image, prior to presenting the image on thedisplay, can communicate the type of the first function used for eachimage, or for each sequence, to a processor performing thepostprocessing of the image. The communication can be done as describedin this application, for example, using wireless communication, orcommunication encoded in the displayed image.

In step 1050, the processor performing preprocessing can apply the firstfunction to the image prior to displaying the image on the virtualproduction display. In step 1060, the processor can present the image onthe virtual production display.

In step 1070, a processor performing postprocessing can record thedisplayed image using a camera to obtain a recorded image. The sameprocessor can perform both preprocessing and postprocessing.

In step 1080, the processor can determine a region within the recordedimage having a pixel value within the range between the threshold andthe second pixel value of the virtual production display.

In step 1090, the processor can increase the dynamic range of the imagerecorded from the virtual production display by applying an inverse ofthe first function to the pixel value of the region, where the inverseof the first function increases the pixel value of the region. Theprocessor can store the increased pixel value of the region in the imagerecorded from the virtual production display, or in a new image createdbased on the image recorded from the virtual production display.

To obtain the first function, the processor can combine one or moreother functions. For example, the processor can obtain second functionmapping the pixel value of the image to the pixel value of the virtualproduction display. The second function can include a power functionsuch as gamma curve used in standard dynamic range, sRGB, PQ function,etc. The gamma transfer function can include 2.2-2.4 gamma. Theprocessor can calculate the first function, which can join the secondfunction at the threshold value, as shown in FIG. 8B. In a pixel valuerange between the first pixel value of the virtual production displayand the threshold, the first function corresponds to the secondfunction. In a pixel value range above the threshold, the first functionproduces a higher pixel value than the second function. The higher pixelvalue corresponds to the desired pixel value range. The first and thesecond function have the same value and the same first derivative at thethreshold value.

To determine the threshold value, the processor can analyze the image todetermine a pixel value infrequently occurring in the image. Theprocessor can assign the infrequently occurring pixel value to thethreshold.

Preferably, illuminating the actors 130 and props 140, 150, 160 in FIG.1A in the virtual production set using an image modified using the firstfunction and presented on the display 110 in FIG. 1A creates believablereflections of the actors and the props. However, if the reflections arenot believable, the reflections can be fixed in postprocessing as donecurrently when the set contains a green screen.

The processor can also obtain the first function by calibrating thedisplay 110 in FIG. 1A, as described in FIGS. 5A-5B. To calibrate thedisplay the processor can obtain a first pixel value of a virtualproduction display region. The processor can obtain a second pixel valueof an image region shown in the virtual production display region. Theprocessor can determine a difference between the first pixel value andthe second pixel value. Based on the difference between the first pixelvalue and the second pixel value, the processor can obtain a calibrationfunction correlating the first pixel value and the second pixel value.To obtain the calibration function, the processor can have multiplefirst pixel values and multiple second pixel values through which to fita function. The processor can determine a region in the recorded imagecorresponding to the virtual production display region. The processorcan increase the pixel value of the first recorded image by applying thefunction to the pixel value of the region in the recorded imagecorresponding to the virtual production display region.

As explained in this application, the first function can vary betweenindividual images, or between sequences in a movie. The processor 135 inFIG. 1B can communicate the type of the first function used in an imageto the processor 155 through wireless communication or imagemodification, as explained in this application. After the processor 135applies the first function in preprocessing, the processor 155 appliesthe inverse of the first function in postprocessing.

To determine which of the brightness increasing techniques described inthis application to apply, the processor can obtain an input indicatinga technique among multiple techniques, where the technique is configuredto increase dynamic range of an image recorded from a virtual productiondisplay. The input can be specified through a dial in both thepreprocessing components and the postprocessing components, or the inputcan be wirelessly communicated between the preprocessing components andthe postprocessing components. Alternatively, the input can becommunicated through the image, or through a synchronization imagepresented prior to presenting the input image on the virtual productiondisplay. Also, the input can be communicated as metadata included in thetime code that is already distributed around the site, including thecamera. The processor performing preprocessing can apply a first portionof the technique to the image prior to displaying the image on thevirtual production display. The processor performing postprocessing canapply a second portion of the technique to an image recorded from thevirtual production display. The first portion and the second portion ofthe technique can be inverses of each other. Multiple techniquesdescribed in this application can be combined in a single image toincrease the dynamic range of the image. The applied techniques can varyfrom frame to frame. In other words, each frame can use a techniquedifferent from the preceding or the succeeding frame.

The processor can also dynamically adjust the capture camera settings toincrease dynamic range of an image recorded from the display. Theprocessor can receive an indication that the first function is a linearfunction such as an identity function, or a multiplier applied to thewhole image. The processor can adjust pixel value of the image recordedfrom the display by adjusting a camera setting, such as the gain settingor camera exposure. If there are any lights on the virtual productionset, the pixel value of the lights can be adjusted in accordance withthe adjustment of camera setting. For example, if the camera settingincreases the brightness of the recorded image, the pixel value of theon-set lights can be decreased, and vice versa.

III. Increasing Dynamic Range of a Virtual Production Display UsingImage Markers

FIG. 11 shows an image containing pixel values exceeding the thresholdpixel value of the display. The image 1100 can include a region 1110whose pixel value exceeds the threshold pixel value of the display 110in FIG. 1A. The region 1110 can take on any linear, curved, orcurvilinear, such as a square, circle, ellipsoid, etc. For example,pixel 1120 has a pixel value matching the threshold pixel value ofdisplay 110, whereas pixel 1130 has a pixel value exceeding thethreshold pixel value of the display.

The display 110 clips the pixel value of the region 1110 as shown ingraph 1140. The X-axis 1150 represents a pixel in the image 1100, suchas pixel 1120, 1130. The Y-axis 1160 represents the pixel value of thedisplayed image. As can be seen in graph 1140, after the pixel value ofthe region 1110, including pixel 1130, exceeds the threshold pixel valueof the display, the display 110 shows all excessive pixel value as thethreshold pixel value of the display, visible in a plateau 1170 of thegraph 1140. The graph 1180 shows the true pixel value of the image 1100that has been clipped to the plateau 1170.

Once the processor 155 records the pixel value represented by the graph1140, the processor 155 cannot reconstruct the pixel value representedby the graph 1180, and represents the pixel value of the region 1110 asthe same pixel value. In one implementation, a machine learning modelcan be trained to detect plateaus, such as plateau 1170, in pixel valuesand to reconstruct the pixel values represented by the graph 1180. Inanother implementation, a processor can modify the image 1100 to includea pattern unlikely to occur in the image, where the pattern signals amodified pixel value. In addition, the pattern can indicate the truemagnitude of the pixel value.

The pattern can be spatial pattern as shown in FIGS. 12 and 13 , or atemporal pattern. To be a temporal pattern, the input image and amodified version of the input image can be displayed at a highfrequency. The camera can be synchronized to record both the input imageand the modified version of the input image. The difference between theinput image and the modified version of the input image can indicate theregions and the original pixel value of the regions in the input image.

FIG. 12 shows a pattern unlikely to occur in the image and indicatingtrue pixel value of the image, according to one embodiment. The graph1200 shows inverse log encoding of region 1110 in FIG. 11 . The X-axis1210 represents a pixel in the image 1100, such as pixel 1120, 1130. TheY-axis 1220 represents the pixel value of the displayed image.

In one implementation, a processor performing preprocessing can obtain athreshold 1230 equal to or less than the threshold pixel value of thedisplay 110. When a pixel value of a pixel in the image 1100 exceeds thethreshold pixel value, the processor modifies the pixel value sent tothe display 110.

To modify the pixel value, the processor determines a difference 1240between the pixel value of the pixel and the threshold pixel value 1230.The processor applies a log function to the difference, thus decreasingthe magnitude of the difference. The log function can be an inversegamma, an inverse PQ function, an inverse HLG function, etc. Theprocessor encodes the result of the log function as a negative offsetfrom the threshold. For example, the processor can subtract the resultof the log function from the threshold and provide the resulting pixelvalue 1250 to the display 110.

Consequently, instead of obtaining a plateau 1170, the true pixel valuerepresented by the graph 1180 is represented using portion 1260 of thegraph 1200. As can be seen, in FIG. 12 , the magnitude of the pixelvalue 1270 (only one labeled for brevity) in the portion 1260 comparedto the plateau 1170 is smaller than the magnitude of the correspondingpixel value 1280 (only one labeled for brevity) in the graph 1180.Reduction in magnitude is due to the log function applied to thedifference 1240.

The graph 1200 with the modified portion 1260 is a pattern unlikely tooccur in the image and signals the magnitude of the true pixel value. Toreconstruct the true pixel value 1180, a processor performingpostprocessing can detect the pattern represented by the graph 1200.Consequently, the processor can reverse the above steps, to obtain thetrue pixel value 1180. To detect the pattern, the processor can utilizea Fast Fourier Transform, cosine transform, Fourier transform, and/ormachine learning, such as convolutional networks, to detect the pattern.

FIG. 13 shows a pattern unlikely to occur in the image and indicatingthe true pixel value of the image, according to another implementation.In addition to, or instead of, the method described in FIG. 12 , aprocessor performing preprocessing can include a signature frequency1300 to indicate that the image pixel value exceeds the display pixelvalue. The X-axis 1310 represents the chroma channel of the image. Thechroma channel can include red, green, and blue, or cyan, yellow,magenta, and black, depending on the image format. The Y-axis 1320represents the magnitude of the chroma channel. When the magnitude ofthe chroma channel of neighboring pixels is plotted, the signaturefrequency 1300 emerges.

The processor can obtain a threshold 1230 equal to or less than themaximum pixel value of the display 110. When a pixel value of a pixel inthe image 1100 exceeds the threshold pixel value, the processor modifiesthe image sent to the display 110. Since most regions having high pixelvalue tend to be white, the processor can encode the signature frequency1300 in the chroma channels. For example, in the region 1110 in FIG. 11, in which the pixel value of the image exceeds the maximum pixel valuethat the display can present, the processor can encode the signaturefrequency 1300 in the chroma channels. The magnitude of the signaturefrequency 1300 can indicate the difference between the pixel value ofthe input image and the threshold, or can indicate the magnitude of thepixel value of the input image. Alternatively, the magnitude of thesignature frequency 1300 can indicate the difference between the pixelvalue of the input image and the maximum pixel value of the display.

The signature frequency 1300 is unlikely to occur in the image 1100. Themagnitude of the signature frequency 1300 can indicate the brightness ofthe region 1110. For example, the difference between the magnitude ofthe two peaks 1330, 1340 can indicate the brightness of the region 1110.A processor performing postprocessing can detect the signature frequencyusing a Fast Fourier Transform or a trained machine learning model.Based on the magnitude of the signature frequency, the processor candetermine the original pixel value of the image 1100.

FIGS. 14A-14B show a flowchart of a method to increase dynamic range ofan image recorded from a display, according to another technique. Instep 1400, a hardware or software processor executing instructionsdescribed in this application can obtain the image to display on thevirtual production display.

In step 1410, the processor can obtain a profile associated with thedisplay, where the profile associated with display indicates a region ofthe display and a range of pixel values that the region of the displaycan present. A pixel value is a set of numbers defining hue, saturationand intensity. The pixel value can be an RGB tuple. To obtain theprofile, the processor can obtain a threshold between the first pixelvalue of the virtual production display and the second pixel value ofthe virtual production display. The second pixel value can be higherthan the first pixel value. The first pixel value can be the minimumpixel value the display can present, while the second pixel value can bethe maximum pixel value the display can present. The minimum and maximumpixel value the display can present can vary depending on what isalready presented on the display. For example, the display can belimited by a total amount of power the display can draw. If there is alarge swath of the display that needs to be at high luminance, themaximum pixel value at any one point of the display can be reducedcompared to when only a small portion of the display needs to be at highluminance.

The first pixel value and the second pixel value indicate a dynamicrange of the virtual production display. The threshold can be higherthan the average of the first pixel value and the second pixel value.For example, the threshold can be a weighted average of the first pixelvalue and the second pixel value, where the second pixel value has aweight higher than 0.5, and the first pixel value has a weight of (1 -the weight of the second pixel value). In another example, the thresholdcan be the second pixel value of the virtual production display. In athird example, the threshold can be 80% of the second pixel value.

In step 1420, the processor can detect a region of the image having anoriginal pixel value exceeding the profile, e.g. dynamic range,associated with the display. A region can be a pixel or a group ofpixels in the image. If the region is a single pixel, the pixel value ofthe region is the pixel value of the pixel. If the region containsmultiple pixels, the pixel value of the region can be a function of thepixel value of each pixel contained in the region. The function can bean average or can be a weighted function. The weighted function can addmore weight to the pixel value of pixels in the middle of the region,and less weight to the pixel value of the pixels on the periphery of theregion.

In step 1430, the processor can modify the region according topredetermined steps producing a pattern unlikely to occur within theimage. The pattern can correspond to the original pixel value. Thepattern can correspond to a difference between the original pixel valueand the threshold. For example, the magnitude of the pattern canindicate the magnitude of the difference between the pixel value of thepixel and the threshold. The pattern can be a spatial or a temporalpattern.

In one implementation, when the original pixel value exceeds the profileassociated with the display, to modify the region, the processor canobtain a difference between the original pixel value and the threshold.The processor can apply a function to the difference to obtain amodified difference, wherein the function reduces a magnitude of thedifference. The function can be a logarithmic function. The processorcan multiply the modified difference by a negative number, such asnegative one. Upon multiplying, the processor can include the modifieddifference in the pattern, thus obtaining a graph similar to graph 1200in FIG. 12 .

In another implementation, when the original pixel value exceeds theprofile associated with the display, to modify the region, the processorcan determine a magnitude of the original pixel value. The processor canobtain a signature frequency indicating that the original pixel valueexceeds the threshold. For example, the signature frequency can bestored in a lookup table the processor can access. The processor canmodify a magnitude of the signature frequency to indicate the magnitudeof the original pixel value. Finally, the processor can encode themodified signature frequency into the channel that exceeds thethreshold, the processor can modify all channels in the same manner whenone of the channels exceeds the threshold, or the processor can modify achroma channel of the image. A channel can be red, green, and blue in anRGB image, cyan, magenta, yellow and black in a CMYK image, etc. Thepixels having a high pixel value tend to be white, and the chromachannels in high pixel value pixels do not carry important information.Therefore, the modifications of the chroma channel in high pixel valuepixels tend to be unnoticed by an observer.

In a third implementation, to modify the region, when the pixel valueexceeds the profile associated with the display, the processor can applya function to the pixel value to obtain a modified pixel value, wherethe function reduces a magnitude of the pixel value. The processor caninclude the modified pixel value in the pattern.

In step 1440, the processor can replace the region of the image with thepattern to obtain a modified image. In step 1450, the processor canpresent the modified image of the virtual production display.

In step 1460, the processor can detect the pattern within the modifiedimage displayed on the virtual production display. In oneimplementation, the processor can use a Fast Fourier Transform to detectthe pattern within the modified image. In another implementation, theprocessor can use a machine learning model to detect the pattern withinthe modified image. The machine learning model can be a convolutionalneural network.

In step 1470, the processor can calculate an expected pixel value of theregion based on the predetermined steps, for example, by reversing thepredetermined steps. The expected pixel value is a recording of thepixel value presented on the display.

In step 1480, the processor can replace the pattern in the modifiedimage with the expected pixel value. In step 1490, the processor canstore the original pixel value.

The processor can calibrate the display. To calibrate the display, theprocessor can obtain a first pixel value of a virtual production displayregion. The processor can obtain a second pixel value of an image regionshown in the virtual production display region. The processor candetermine a difference between the first pixel value and the secondpixel value. Based on the difference between the first pixel value andthe second pixel value, the processor can obtain a calibration functioncorrelating the first pixel value and the second pixel value. To obtainthe calibration function, the processor can have multiple first pixelvalues and multiple second pixel values through which to fit a function.The processor can record the modified image from the virtual productiondisplay to obtain a recorded image. The processor can determine a regionin the recorded image corresponding to the virtual production displayregion. The processor can increase the pixel value of the first recordedimage by applying the function to the pixel value of the region in therecorded image corresponding to the virtual production display region.

The processor can calibrate the camera to determine how the patternoccurs in the recorded image. To calibrate the camera, the processor canrecord the pattern from the display. The processor can determine asignature pixel value of the recorded pattern, and can store thesignature pixel value of the recorded pattern to use in detecting thepattern within the modified image presented on the display. Theprocessor can also calibrate the threshold 1230 in FIG. 12 to determinethe pixel value of the threshold in the recorded image.

When the image is being presented on the display, the processor canpreserve the average pixel value of a third region of the image, wherethe third region includes the region and a second region surrounding theregion. The reason to preserve the average pixel value of the third,bigger, region is to provide appropriate lighting to the actors andprops within the virtual production set. Once the camera records themodified image, the processor associated with the camera can increasethe pixel value of the region, while decreasing the pixel value of thesecond region. To accomplish this task, the processor can determine thesecond region surrounding the region. The processor can increase pixelvalue of the second region to obtain an increased pixel value, where theincrease in the pixel value of the second region corresponds to adecrease in pixel value of the region. Thereby, the processor preservesthe average pixel value of the third region. The processor can includethe increased pixel value and a second pattern to indicate the increasein the pixel value of the second region in the modified image. Theprocessor can present the modified image on the display. The camera canrecord the modified image. The processor can detect the second patternwithin the modified image presented on the display. The processor cancalculate a second expected pixel value of the second region, where thesecond expected pixel value is lower than the pixel value of the secondregion. Finally, the processor can replace the second pattern in themodified image with the second expected pixel value.

The processor can train a machine learning model to detect a discrepancybetween a first image and a second image, where the first image excludesthe pattern, and the second image includes the first image modified withthe pattern. The processor can use the machine learning model to detectthe pattern within the image.

The processor can adjust the pattern to include in the modified image,based on the input image. The processor can analyze the input image todetermine the pattern unlikely to occur within the image, where thepattern substantially preserves appearance of the input image. Tosubstantially preserve the appearance of the input image, the processorcan confine the modifications to the image to be within 30% of theoriginal color, or 30% of the original pixel value of the input image.To determine the pattern to include in the modified image, the processorcan use machine learning to analyze the image and determine the patternthat is least disruptive to the input image.

The processor can create the pattern unlikely to occur within the inputimage. The pattern can indicate the region to be modified, a method usedto modify the input image, and the expected pixel value of the region.

To determine which of the brightness increasing techniques described inthis application to apply, the processor can obtain an input indicatinga technique among multiple techniques, where the technique is configuredto increase dynamic range of an image recorded from a virtual productiondisplay. The input can be specified through a dial in both thepreprocessing components and the postprocessing components, or the inputcan be wirelessly communicated between the preprocessing components andthe postprocessing components. Alternatively, the input can becommunicated through the image, or through a synchronization imagepresented prior to presenting the input image on the virtual productiondisplay. The processor performing preprocessing can apply a firstportion of the technique to the image prior to displaying the image onthe virtual production display. The processor performing postprocessingcan apply a second portion of the technique to an image recorded fromthe virtual production display. The first portion and the second portionof the technique can be inverses of each other.

Once the image is recorded, the processor associated with the camera canreverse the steps performed by the processor associated with thedisplay. To calculate the expected pixel value of the recorded image,the processor can obtain the pattern to detect. Based on the pattern,the processor can obtain a function to apply to the modified region. Thefunction can the inverse of the function applied to the image togenerate the pattern. For example, the function can include multiplyingthe modified region, such as region 1200 in FIG. 12 by a negative numberand calculating an exponent of the pixel values in the region 1200, toobtain the original pixel value 1180 in FIG. 12 .

Similarly, the processor can obtain a signature frequency to detect,such as frequency 1300 in FIG. 13 . The processor can detect thesignature frequency indicating the modified region. The processor candetermine a magnitude of the signature frequency, where the magnitude ofthe signature frequency indicates the expected pixel value. For example,the difference between the two peaks 1330, 1340 in FIG. 13 can indicatethe magnitude of the expected pixel value. Based on the magnitude of thesignature frequency, the processor can modify a pixel value of themodified region to obtain the expected pixel value.

IV. Increasing Dynamic Range of a Virtual Production Display UsingMetadata

FIG. 15 shows a system to increase dynamic range of an image recordedfrom a virtual production display, according to another implementation.An input image 1500 can include a region 1510 of high pixel value, and aregion 1520 of low pixel value. The display 1530 has a limited range ofpixel values, such as between 0 and 100, where 0 can be the first pixelvalue of the display, and 100 can be the second pixel value of thedisplay. The second pixel value can be higher than the first pixelvalue. The first pixel value can be the minimum pixel value, while thesecond pixel value can be the maximum pixel value. The region 1510 ofhigh pixel value can have pixel value in the range 101-200, while theregion 1520 of low pixel value can have pixel value in the range 0-100.

To preserve the veracity of the high pixel value region 1510, aprocessor 1540 performing preprocessing can obtain a threshold 1550between a minimum and a second pixel value of the display 1530. Thethreshold is greater than the first pixel value of the display 1530 andcan be equal to the second pixel value of the display. The processor1540 can compare the pixel value of regions 1560 (only one labeled forbrevity) of the input image 1500 against the threshold. The region 1560can be a pixel or the region can be a group of pixels, such as a groupof neighboring pixels. When the region 1560 includes a group of pixels,to compute the pixel value of the region 1560, the processor can computean average of each pixel’s pixel value, or the processor can compute aweighted average of each pixel’s pixel value. For example, pixels in thecenter of the region 1560 can have a higher weight than the pixels onthe periphery.

When the pixel value of a region 1560 exceeds the threshold 1550, theprocessor can record an indication of the region’s pixel value and anindication of the region in a representation 1570 of the input image1500. The indication of the region’s pixel value can be the originalpixel value of the region 1560, or a difference between the thresholdand the original pixel value of the region 1560. The indication of theregion 1560 can be a location of the region in the input image 1500. Forexample, if the region 1560 is a pixel, the location of the region canbe the pixel coordinates in the input image 1500. If the region 1560 isa group of pixels, the location can be the pixel coordinates of thecentral pixel in the group of pixels as well as the width and height ofthe region.

The representation 1570 occupies less memory than the input image 1500because the representation 1570 is compact. While the input image 1500contains at least three color values for each pixel, the representation1570 can contain only one value, namely pixel value, and only forregions 1560 whose pixel value exceeds the threshold 1550. Consequently,the representation 1570 is sparse compared to the input image 1500 andcan be efficiently communicated between the processor 1540 performingpreprocessing, and a processor 1580 performing postprocessing.

Once the processor 1540 analyzes the input image 1500 and creates therepresentation 1570 indicating regions 1560 exceeding the thresholdpixel value in the input image, the processor can cause the display 1530to present an image 1590 corresponding to the input image 1500, butwhere the pixel value regions exceeding the second pixel value of thedisplay are clipped. For example, if the pixel value of the region 1560is 153, while the second pixel value of the display 1530 is 100, theimage 1590 presents the pixel value of the region 1560A as 100.

The camera 1505 can record the image 1590 to obtain recorded image 1515,in which the pixel value of the region 1560 is represented as 100. Theprocessor 1580 can obtain the recorded image 1515 and the representation1570 via a wired or a wireless network connecting processors 1540 and1580.

From the representation 1570, the processor 1580 can retrieve theindication of the pixel value of the region 1560 and the indication ofthe region. Based on the indication of the pixel value of the region1560, the processor 1580 can calculate the region’s original pixelvalue. Based on the indication of the region, the processor 1580 canidentify the region 1560 and can replace the pixel value of the region1560 with the newly calculated pixel value to obtain the output image1525, as explained below. The output image 1525 can be an exact match ora close approximation of the input image 1500.

FIG. 16 shows a correspondence between an input image and a recordedimage. The camera 1505 in FIG. 15 can record the input image 1600 toproduce the recorded image 1610. The input image 1600 can include pixels1600A, 1600B, 1600C, 1600D, while the recorded image can include pixels1610A, 1610B, 1610C, 1610D. Due to the blur of the camera 1505 lens, thepixels 1610A, 1610B, 161 0C, 1610D in the recorded image 1610 do notcorrespond exactly to the pixels 1600A, 1600B, 1600C, 1600D in the inputimage 1600.

For example, a camera lens blur can cause a pixel 1610A to be a weightedsum of pixels 1600A, 1600B, 1600C, 1600D. For example, pixel 1600A canhave the highest influence, pixels 1600B, 1600C can have lowerinfluence, while pixel 1600D can have the lowest influence.Consequently, pixel 1610A is lighter than pixel 1600A, but darker thanpixel 1600B. The map between pixel 1610A and pixels 1600A, 1600B, 1600C,1600D can be:

.5 * 1600A +.2*1600B +.2*1600C +.1*1600D.

To determine a correspondence between the input image 1600 and therecorded image 1610, the processor can calibrate the input image to theoutput image and determine the map (1). The map (1) can vary from pixelto pixel. For each pixel 1610A-D (only four labeled for brevity) in therecorded image 1610, the processor can determine the pixels 1600A-D(only four labeled for brevity) in the input image 1600 that contributeto the pixels 1610A-D. Further, the processor can determine the map,namely, the amount of contribution of each of the pixels 1600A-D to thepixel 1610A.

Once the processor determines the map (1), the processor can use theindication of the location and the indication of the pixel value in therepresentation 1570 in FIG. 15 to obtain the output image 1525 in FIG.15 .

FIG. 17 shows a process to retrieve the original pixel value of theinput image using a map and a representation of the input image. Theinput image 1700 contains the region 1710 of high pixel value includingpixels 1700A, 1700B that are clipped by the display. Presented image1720 is the image presented on the display, where the region 1710 has alower pixel value 1730. Recorded image 1740 is recorded by a camera, andcontains blurred pixels 1740A, 1740B.

A processor can receive representation 1750 including an original pixelvalue and a location of clipped pixels 1700A, 1700B. In addition, theprocessor can obtain a map 1760 which indicates the pixels 1740A, 1740Bwhose pixel value is affected by clipped pixels 1700A, 1700B. Further,the map 1760 indicates by how much pixels 1700A, 1700B affect the pixelvalue of pixels 1740A, 1740B.

For example, the map can indicate that pixel 1700A affects the pixelvalue of pixel 1740A by a factor of 0.5. In one implementation, theprocessor can assume that the pixel value of the pixel 1720A,corresponding to the clipped pixel 1700A, is clipped to the maximumvalue of the display. Consequently, the processor can increase the pixelvalue of the pixel 1740A by:

(original pixel value of pixel 1710A - maximum pixel value of the display) * 0.5.

to obtain pixel 1770A in the output image 1770. In anotherimplementation, the processor can obtain the pixel value of thepresented pixel 1720A by, for example, obtaining a power provided to thepixel 1720A. Consequently, the processor can increase the pixel value ofthe pixel 1740A by:

(original pixel value of pixel 1710A - pixel value of the presented pixel 1720A) * 0.5.

to obtain pixel 1770A in the output image 1770. The processor canperform the adjustment for every pixel in the recorded image 1740 thatis affected by a pixel contained in the representation 1750 to obtainthe output image 1770.

FIG. 18 shows a representation of the input image. The representation1800 of the input image can include an indication of the region 1810 ofthe input image and an indication of the original pixel value 1820 ofthe region. The indication of the region 1810 can include X-coordinate1830 and Y-coordinate 1840 of the bright pixel, such as 1053, 546. Theindication of the original pixel value 1820 can include the originalpixel values such as 196.5. To represent all the bright pixels in theinput image, the representation 1800 can contain multiple sets of threenumbers for each bright pixel.

FIG. 19 shows a map 1900 between a pixel in the representation 1800 inFIG. 18 , and a pixel in a recorded image. The map 1900 can be the map1760 in FIG. 17 . The map 1900 can be a two-dimensional data structure,such as an array, that can take as input the indication of the region1830, 1840 in FIG. 18 and produce as output a list of pixels affected bythe region 1810, and the magnitude of the influence of the region 1810on each pixel in the list of pixels.

For example, the region 1810 influences the pixels in the list 1910. Thelist 1910 identifies each pixel influenced by, for example, coordinatesof the pixel, and shows the magnitude of the influence of the region1810 on each pixel. The list 1910 can include a 3-tuple indicating thelocation of the pixel as well as the magnitude of influence. Themagnitude of influence can be used in the calculations (2) and (3) asdescribed in this application and can be greater than 0 and less than orequal to 1.

FIGS. 20A-20B show a flowchart of a method to increase dynamic range ofan image recorded from a display, according to another technique. Instep 2000, a processor can obtain an input image to display on thevirtual production display. The input image, as used in thisspecification, can be a description of a scene to display represented inanycolor space such as RGB, CMYK. The input image, is using thespecification, can be a description of a scene to display representedusing spectral data, namely, an indication of a particularelectromagnetic wavelength and an indication of intensity at theparticular electromagnetic wavelength. To convert the spectral data intoa color space, the processor can integrate the intensities at theappropriate wavelengths to obtain intensity of a color used in a colorspace.

In step 2010, the processor can obtain a profile associated with thedisplay. The profile associated with display indicates a region of thedisplay and a range of pixel values that the region of the display canpresent. A pixel value is a set of numbers defining hue, saturation andintensity. The profile can include a threshold between a first pixelvalue of the virtual production display and a second pixel value of thevirtual production display. The second pixel value can be higher thanthe first pixel value. The first pixel value can be the minimum pixelvalue, while the second pixel value can be the maximum pixel value. Thefirst pixel value and the second pixel value indicate a dynamic range ofthe virtual production display. The threshold can be higher than theaverage of the first pixel value and the second pixel value. Forexample, the threshold can be a weighted average of the first pixelvalue and the second pixel value, where the second pixel value has aweight higher than 0.5, and the first pixel value has a weight of (1 -the weight of the second pixel value). In another example, the thresholdcan be the second pixel value of the virtual production display. In athird example, the threshold can be 80% of the second pixel value.

In step 2020, the processor can detect a region of the input imagehaving an original pixel value exceeding the profile associated with thedisplay, such as is exceeded the threshold included in the profile. Aregion can be a pixel or a group of pixels in the image. If the regionis a single pixel, the pixel value of the region is the pixel value ofthe pixel. If the region contains multiple pixels, the pixel value ofthe region can be a function of pixel value of each pixel contained inthe region. The function can be an average or can be a weightedfunction. The weighted function can add more weight to the pixel valueof the pixels in the middle of the region, and less weight to the pixelvalue of the pixels on the periphery of the region.

In step 2030, the processor can create a representation of the inputimage including an indication of the region of the input image and anindication of the original pixel value of the region. The representationof the input image can be a data structure as explained in FIG. 18 . Therepresentation of the input image occupies less memory than the inputimage. To create the representation of the input image, the processorcan store a location of the region of the input image and the indicationof the original pixel value of the region in the representation of theinput image.

In step 2040, the processor can present on the virtual productiondisplay the input image including the region of the image having theoriginal pixel value original pixel value exceeding the profileassociated with the display. The original pixel value of the region inthe presented image can be clipped, that is, presented at a lower pixelvalue than the original pixel value.

In step 2050, the processor can send the representation of the inputimage to a processor associated with the camera recording the presentedimage. The processor can send the representation of the input image viaa channel independent of the display, such as a wired or a wirelessnetwork.

In step 2060, the processor can record the presented image by the camerato obtain a recorded image. In step 2070, the processor can receive therepresentation of the input image.

In step 2080, the processor can increase dynamic range of the recordedimage by modifying the recorded image based on the representation of theinput image to obtain an output image. The output image can closelyapproximate, or exactly match, the pixel value of the input image. Forexample, if the pixel value of the output image closely approximates thepixel value of the input image, the pixel value of the output image canbe ±20% of the pixel value of the original image. To modify the recordedimage, the processor can retrieve from the representation of the inputimage the first location and the indication of the original pixel valueof the region. Based on the map and the first location, the processorcan determine the second location. Based on the map and the indicationof the original pixel value of the region, the processor can determinethe second pixel value as explained in this application, for example inFIG. 19 . The processor can replace a pixel value at the second locationof the recorded image with the second pixel value.

When the camera is moving, or when the camera is viewing the display atan angle, the processor can take into account the relative position andrelative orientation between the display and the camera to increase thedynamic range of the recorded image. The processor can obtain a relativeposition and a relative orientation between the display and the camera.Based on the relative position and the relative orientation between thedisplay and the camera, the processor can modify the recorded imageusing the representation of the input image to obtain the output image.

The processor can distinguish between the display and props and actorsplaced in front of the display, invisible to the camera. The processorcan preserve the props and acceptors placed in front of the display inthe recorded image. Specifically, the processor can identify a regionassociated with the recorded image and indicating an object, e.g. propsand actors, disposed between the display and the camera. The processorcan preserve the region associated with the recorded image even if aportion of the region is included in the representation of the inputimage.

The processor can perform various calibrations. For example, theprocessor can calibrate the camera, and/or the processor can calibratethe virtual production display.

To calibrate the camera, the processor can obtain a first pixel value ofa first region in the input image, and a second pixel value of a secondregion in the recorded image corresponding to the first region. Theprocessor can determine a difference between the first pixel value andthe second pixel value. Based on the difference between the first pixelvalue and the second pixel value, the processor can obtain a map. Asexplained in this application, for example in FIG. 19 , the map cancorrelate the first pixel value and the second pixel value and a firstlocation of the region associated with the first pixel value, and asecond location of the region associated with the second pixel value.

To calibrate the display, the processor can display various images, asexplained in this application, for example in FIGS. 5A-5B. The displaycan be split into panels, where each panel is a separate power source,thus increasing the brightness of the display. Each panel can becalibrated independently. The processor can obtain, e.g., measure, afirst pixel value of a virtual production display region, and a secondpixel value of an input image region shown in the virtual productiondisplay region. The processor can determine a difference between thefirst pixel value and the second pixel value. There can be multiple setsof first pixel value and second pixel value, for the processor to beable to fit a curve. Based on the difference between the first pixelvalue and the second pixel value, the processor can obtain a functioncorrelating the first pixel value and the second pixel value. Theprocessor can determine a region in the recorded image corresponding tothe virtual production display region. The processor can increase thepixel value of the recorded image by applying the function to a pixelvalue of the region in the recorded image corresponding to the virtualproduction display region.

To determine which of the brightness increasing techniques described inthis application to apply, the processor can obtain an input indicatinga technique among multiple techniques, where the technique is configuredto increase dynamic range of an image recorded from a virtual productiondisplay. The input can be specified through a dial in both thepreprocessing components and the postprocessing components, or the inputcan be wirelessly communicated between the preprocessing components andthe postprocessing components. Alternatively, the input can becommunicated through the image, or through a synchronization imagepresented prior to presenting the input image on the virtual productiondisplay. The processor performing preprocessing can apply a firstportion of the technique to the image prior to displaying the image onthe virtual production display. The processor performing postprocessingcan apply a second portion of the technique to an image recorded fromthe virtual production display. The first portion and the second portionof the technique can be inverses of each other.

The processor can generate a unique identifier associated with thepresented image and a time indicating when the input image is presentedon the display. The processor can enable the camera to determine thepresented image based on the unique identifier and the time indicatingwhen the input image is presented on display, by sending to the camerathe unique identifier and the time indicating when the input image ispresented on the display. The processor can include an indicationassociated with the unique identifier and the time in therepresentation. The processor can obtain a relative position and arelative orientation between the display and the camera. The processorcan include the relative position and the relative orientation in therepresentation.

Calibrating an Interaction Between a Display and a Camera

FIGS. 21A-21B show a flowchart of a method to calibrate an interactionbetween a display and a camera. In step 2100, a hardware or softwareprocessor executing instructions described in this application canpresent an input image on the display to obtain a presented image. Instep 2110, the processor can record the presented image to obtain arecorded image. The camera can be calibrated or uncalibrated, and thecamera can be arbitrarily positioned relative to the display. Forexample, the camera can be viewing the display at a non-perpendicularangle.

In step 2120, the processor can obtain the input image via a channeldifferent from the display. The channel can be a wired or a wirelessnetwork. For example, the processor can receive the image presented ondisplay through the network, or the processor can receive an indicationof the image presented a display, such as a unique identifier. Theprocessor can then use the unique identifier to retrieve the imagepresented on the display from a database.

In step 2130, the processor can obtain an indication of a display regionassociated with the display. The region can be a pixel or a collectionof pixels such as an LED block associated with the virtual productiondisplay. The region can also include noncontiguous pixels. Theindication of the display region can be a unique identifier of thedisplay region, such as a location of the display region.

In step 2140, the processor can determine an input image regioncorresponding to the display region, and a recorded image regioncorresponding to the display region.

In step 2150, the processor can obtain a first pixel value associatedwith the input image region and a second pixel value associated with therecorded image region. A pixel value is a set of numbers defining hue,saturation and intensity. If the region is a pixel, the pixel value canrepresent a single pixel. If the region includes multiple pixels, thepixel value can be a group of pixel values representing each pixel inthe region, or the pixel value can be an average of at least some of thepixels included in the region.

In step 2160, the processor can determine a mapping between the firstpixel value and the second pixel value, where applying the mapping tothe second pixel value substantially produces the first pixel value.Substantially producing the first pixel value can include matching thefirst pixel value to within 90%.

In step 1270, the processor can store an identifier associated with therecorded image region and the mapping.

The calibration process can aid in selecting an appropriate displayregion, such as an LED block to include in the virtual productiondisplay. For example, the processor can determine a color profile of thedisplay region. The color profile can indicate a range of hue,saturation, and intensity that display region is capable of presenting.The processor can determine presented image region corresponding to thedisplay region. The processor can obtain a third pixel value associatedwith the presented image region. The processor can determine a secondmapping between the first pixel value and the third pixel value, whereapplying the second mapping to the third pixel value substantiallyproduces the first pixel value. The second mapping indicates the colorprofile of the display region. The processor can obtain a desiredmapping between the first pixel value and the third pixel value, wherethe desired mapping indicates a range of values associated with thethird pixel value. The desired mapping can indicate the desired colorprofile of the display region. The processor can determine whether thedesired mapping includes the second mapping. Upon determining thedesired mapping does not include the second mapping, the display regioncan be adjusted. To adjust display region, the display region can beremoved from the wall, adjusted to match the desired mapping, or put ina specific location on the wall based on the color profile of thedisplay region.

The desired mapping can indicate the desired color profile of thedisplay region. The desired color profile can vary based on location.For example, the processor can obtain a location of display region onthe display. Based on the location, the processor can determine thedesired mapping. In a more specific example, the desired mapping of adisplay region in the upper half of the display can indicate that theLED blocks placed in the upper half of the display need to have a highrange in blue because the sky tends to be displayed in the upper half ofthe display. In another specific example, the desired color profile canindicate that LED blocks having a high dynamic range and capable ofproducing high intensity images should be placed in the upper half ofthe screen, where the sun and the sky tend to be displayed.

The desired color profile can vary based on the neighboring displayregions. The color profiles of display regions should vary smoothlyacross the display. The processor can obtain a third mapping associatedwith a third presented image region neighboring the presented imageregion, where the third mapping indicates a color profile associatedwith the third presented image region. The processor can determine thedesired mapping by computing the range of values based on the thirdmapping, where the desired mapping produces a smooth transition betweenthe presented image region and the third presented image region.

The mapping between the input image and the recorded image can varybased on relative position and orientation between the display and thecamera. The processor can obtain relative position and a relativeorientation between the display and the camera. The processor candetermine a correspondence indicating how the mapping varies based onthe relative position and the relative orientation. The correspondencecan be a lookup table or a mathematical function. The processor canstore the identifier associated with the recorded image region, themapping, and the correspondence.

The processor can modify the next image based on the results of thecalibration. For example, the processor can record a second presentedimage presented on the display to obtain a second recorded image. Theprocessor can retrieve the identifier associated with the recorded imageregion and the mapping. Based on the identifier, the processor candetermine a second recorded image region associated with the secondrecorded image. The processor can apply the mapping to the secondrecorded image region to obtain an adjusted recorded image region. Theprocessor can store the adjusted recorded image region.

Visual Content Generation System

FIG. 22 illustrates an example visual content generation system 2200 asmight be used to generate imagery in the form of still images and/orvideo sequences of images. Visual content generation system 2200 mightgenerate imagery of live action scenes, computer generated scenes, or acombination thereof. In a practical system, users are provided withtools that allow them to specify, at high levels and low levels wherenecessary, what is to go into that imagery. For example, a user might bean animation artist and might use visual content generation system 2200to capture interaction between two human actors performing live on asound stage and replace one of the human actors with acomputer-generated anthropomorphic non-human being that behaves in waysthat mimic the replaced human actor’s movements and mannerisms, and thenadd in a third computer-generated character and background sceneelements that are computer-generated, all in order to tell a desiredstory or generate desired imagery.

Still images that are output by visual content generation system 2200might be represented in computer memory as pixel arrays, such as atwo-dimensional array of pixel color values, each associated with apixel having a position in a two-dimensional image array. Pixel colorvalues might be represented by three or more (or fewer) color values perpixel, such as a red value, a green value, and a blue value (e.g., inRGB format). Dimensions of such a two-dimensional array of pixel colorvalues might correspond to a preferred and/or standard display scheme,such as 1920-pixel columns by 1280-pixel rows or 4096-pixel columns by2160-pixel rows, or some other resolution. Images might or might not bestored in a certain structured format, but either way, a desired imagemay be represented as a two-dimensional array of pixel color values. Inanother variation, images are represented by a pair of stereo images forthree-dimensional presentations and in other variations, an imageoutput, or a portion thereof, might represent three-dimensional imageryinstead of just two-dimensional views. In yet other embodiments, pixelvalues are data structures and a pixel value can be associated with apixel and can be a scalar value, a vector, or another data structureassociated with a corresponding pixel. That pixel value might includecolor values, or not, and might include depth values, alpha values,weight values, object identifiers or other pixel value components.

A stored video sequence might include a plurality of images such as thestill images described above, but where each image of the plurality ofimages has a place in a timing sequence and the stored video sequence isarranged so that when each image is displayed in order, at a timeindicated by the timing sequence, the display presents what appears tobe moving and/or changing imagery. In one representation, each image ofthe plurality of images is a video frame having a specified frame numberthat corresponds to an amount of time that would elapse from when avideo sequence begins playing until that specified frame is displayed. Aframe rate might be used to describe how many frames of the stored videosequence are displayed per unit time. Example video sequences mightinclude 24 frames per second (24 FPS), 50 FPS, 140 FPS, or other framerates. In some embodiments, frames are interlaced or otherwise presentedfor display, but for clarity of description, in some examples, it isassumed that a video frame has one specified display time, but othervariations might be contemplated.

One method of creating a video sequence is to simply use a video camerato record a live action scene, i.e., events that physically occur andcan be recorded by a video camera. The events being recorded can beevents to be interpreted as viewed (such as seeing two human actors talkto each other) and/or can include events to be interpreted differentlydue to clever camera operations (such as moving actors about a stage tomake one appear larger than the other despite the actors actually beingof similar build, or using miniature objects with other miniatureobjects so as to be interpreted as a scene containing life-sizedobjects).

Creating video sequences for story-telling or other purposes often callsfor scenes that cannot be created with live actors, such as a talkingtree, an anthropomorphic object, space battles, and the like. Such videosequences might be generated computationally rather than capturing lightfrom live scenes. In some instances, an entirety of a video sequencemight be generated computationally, as in the case of acomputer-animated feature film. In some video sequences, it is desirableto have some computer-generated imagery and some live action, perhapswith some careful merging of the two.

While computer-generated imagery might be creatable by manuallyspecifying each color value for each pixel in each frame, this is likelytoo tedious to be practical. As a result, a creator uses various toolsto specify the imagery at a higher level. As an example, an artist mightspecify the positions in a scene space, such as a three-dimensionalcoordinate system, of objects and/or lighting, as well as a cameraviewpoint, and a camera view plane. From that, a rendering engine couldtake all of those as inputs, and compute each of the pixel color valuesin each of the frames. In another example, an artist specifies positionand movement of an articulated object having some specified texturerather than specifying the color of each pixel representing thatarticulated object in each frame.

In a specific example, a rendering engine performs ray tracing wherein apixel color value is determined by computing which objects lie along aray traced in the scene space from the camera viewpoint through a pointor portion of the camera view plane that corresponds to that pixel. Forexample, a camera view plane might be represented as a rectangle havinga position in the scene space that is divided into a grid correspondingto the pixels of the ultimate image to be generated, and if a raydefined by the camera viewpoint in the scene space and a given pixel inthat grid first intersects a solid, opaque, blue object, that givenpixel is assigned the color blue. Of course, for moderncomputer-generated imagery, determining pixel colors - and therebygenerating imagery - can be more complicated, as there are lightingissues, reflections, interpolations, and other considerations.

As illustrated in FIG. 22 , a live action capture system 2202 captures alive scene that plays out on a stage 2204. Live action capture system2202 is described herein in greater detail, but might include computerprocessing capabilities, image processing capabilities, one or moreprocessors, program code storage for storing program instructionsexecutable by the one or more processors, as well as user input devicesand user output devices, not all of which are shown.

In a specific live action capture system, cameras 2206(1) and 2206(2)capture the scene, while in some systems, there might be other sensor(s)2208 that capture information from the live scene (e.g., infraredcameras, infrared sensors, motion capture (“mo-cap”) detectors, etc.).On stage 2204, there might be human actors, animal actors, inanimateobjects, background objects, and possibly an object such as a greenscreen 2210 that is designed to be captured in a live scene recording insuch a way that it is easily overlaid with computer-generated imagery.Stage 2204 might also contain objects that serve as fiducials, such asfiducials 2212(1)-(3), that might be used post-capture to determinewhere an object was during capture. A live action scene might beilluminated by one or more lights, such as an overhead light 2214.

During or following the capture of a live action scene, live actioncapture system 2202 might output live action footage to a live actionfootage storage 2220. A live action processing system 2222 might processlive action footage to generate data about that live action footage andstore that data into a live action metadata storage 2224. Live actionprocessing system 2222 might include computer processing capabilities,image processing capabilities, one or more processors, program codestorage for storing program instructions executable by the one or moreprocessors, as well as user input devices and user output devices, notall of which are shown. Live action processing system 2222 might processlive action footage to determine boundaries of objects in a frame ormultiple frames, determine locations of objects in a live action scene,where a camera was relative to some action, distances between movingobjects and fiducials, etc. Where elements have sensors attached to themor are detected, the metadata might include location, color, andintensity of overhead light 2214, as that might be useful inpost-processing to match computer-generated lighting on objects that arecomputer-generated and overlaid on the live action footage. Live actionprocessing system 2222 might operate autonomously, perhaps based onpredetermined program instructions, to generate and output the liveaction metadata upon receiving and inputting the live action footage.The live action footage can be camera-captured data as well as data fromother sensors.

An animation creation system 2230 is another part of visual contentgeneration system 2200. Animation creation system 2230 might includecomputer processing capabilities, image processing capabilities, one ormore processors, program code storage for storing program instructionsexecutable by the one or more processors, as well as user input devicesand user output devices, not all of which are shown. Animation creationsystem 2230 might be used by animation artists, managers, and others tospecify details, perhaps programmatically and/or interactively, ofimagery to be generated. From user input and data from a database orother data source, indicated as a data store 2232, animation creationsystem 2230 might generate and output data representing objects (e.g., ahorse, a human, a ball, a teapot, a cloud, a light source, a texture,etc.) to an object storage 2234, generate and output data representing ascene into a scene description storage 2236, and/or generate and outputdata representing animation sequences to an animation sequence storage2238.

Scene data might indicate locations of objects and other visualelements, values of their parameters, lighting, camera location, cameraview plane, and other details that a rendering engine 2250 might use torender CGI imagery. For example, scene data might include the locationsof several articulated characters, background objects, lighting, etc.specified in a two-dimensional space, three-dimensional space, or otherdimensional space (such as a 2.5-dimensional space, three-quarterdimensions, pseudo-3D spaces, etc.) along with locations of a cameraviewpoint and view place from which to render imagery. For example,scene data might indicate that there is to be a red, fuzzy, talking dogin the right half of a video and a stationary tree in the left half ofthe video, all illuminated by a bright point light source that is aboveand behind the camera viewpoint. In some cases, the camera viewpoint isnot explicit, but can be determined from a viewing frustum. In the caseof imagery that is to be rendered to a rectangular view, the frustumwould be a truncated pyramid. Other shapes for a rendered view arepossible and the camera view plane could be different for differentshapes.

Animation creation system 2230 might be interactive, allowing a user toread in animation sequences, scene descriptions, object details, etc.and edit those, possibly returning them to storage to update or replaceexisting data. As an example, an operator might read in objects fromobject storage into a baking processor 2242 that would transform thoseobjects into simpler forms and return those to object storage 2234 asnew or different objects. For example, an operator might read in anobject that has dozens of specified parameters (movable joints, coloroptions, textures, etc.), select some values for those parameters andthen save a baked object that is a simplified object with now fixedvalues for those parameters.

Rather than requiring user specification of each detail of a scene, datafrom data store 2232 might be used to drive object presentation. Forexample, if an artist is creating an animation of a spaceship passingover the surface of the Earth, instead of manually drawing or specifyinga coastline, the artist might specify that animation creation system2230 is to read data from data store 2232 in a file containingcoordinates of Earth coastlines and generate background elements of ascene using that coastline data.

Animation sequence data might be in the form of time series of data forcontrol points of an object that has attributes that are controllable.For example, an object might be a humanoid character with limbs andjoints that are movable in manners similar to typical human movements.An artist can specify an animation sequence at a high level, such as“the left hand moves from location (X1, Y1, Z1) to (X2, Y2, Z2) overtime T1 to T2”, at a lower level (e.g., “move the elbow joint 2.5degrees per frame”) or even at a very high level (e.g., “character Ashould move, consistent with the laws of physics that are given for thisscene, from point P1 to point P2 along a specified path”).

Animation sequences in an animated scene might be specified by whathappens in a live action scene. An animation driver generator 2244 mightread in live action metadata, such as data representing movements andpositions of body parts of a live actor during a live action scene.Animation driver generator 2244 might generate corresponding animationparameters to be stored in animation sequence storage 2238 for use inanimating a CGI object. This can be useful where a live action scene ofa human actor is captured while wearing mo-cap fiducials (e.g.,high-contrast markers outside actor clothing, high-visibility paint onactor skin, face, etc.) and the movement of those fiducials isdetermined by live action processing system 2222. Animation drivergenerator 2244 might convert that movement data into specifications ofhow joints of an articulated CGI character are to move over time.

A rendering engine 2250 can read in animation sequences, scenedescriptions, and object details, as well as rendering engine controlinputs, such as a resolution selection and a set of renderingparameters. Resolution selection might be useful for an operator tocontrol a trade-off between speed of rendering and clarity of detail, asspeed might be more important than clarity for a movie maker to testsome interaction or direction, while clarity might be more importantthan speed for a movie maker to generate data that will be used forfinal prints of feature films to be distributed. Rendering engine 2250might include computer processing capabilities, image processingcapabilities, one or more processors, program code storage for storingprogram instructions executable by the one or more processors, as wellas user input devices and user output devices, not all of which areshown.

Visual content generation system 2200 can also include a merging system2260 that merges live footage with animated content. The live footagemight be obtained and input by reading from live action footage storage2220 to obtain live action footage, by reading from live action metadatastorage 2224 to obtain details such as presumed segmentation in capturedimages segmenting objects in a live action scene from their background(perhaps aided by the fact that green screen 2210 was part of the liveaction scene), and by obtaining CGI imagery from rendering engine 2250.

A merging system 2260 might also read data from rulesets formerging/combining storage 2262. A very simple example of a rule in aruleset might be “obtain a full image including a two-dimensional pixelarray from live footage, obtain a full image including a two-dimensionalpixel array from rendering engine 2250, and output an image where eachpixel is a corresponding pixel from rendering engine 2250 when thecorresponding pixel in the live footage is a specific color of green,otherwise output a pixel value from the corresponding pixel in the livefootage.”

Merging system 2260 might include computer processing capabilities,image processing capabilities, one or more processors, program codestorage for storing program instructions executable by the one or moreprocessors, as well as user input devices and user output devices, notall of which are shown. Merging system 2260 might operate autonomously,following programming instructions, or might have a user interface orprogrammatic interface over which an operator can control a mergingprocess. In some embodiments, an operator can specify parameter valuesto use in a merging process and/or might specify specific tweaks to bemade to an output of merging system 2260, such as modifying boundariesof segmented objects, inserting blurs to smooth out imperfections, oradding other effects. Based on its inputs, merging system 2260 canoutput an image to be stored in a static image storage 2270 and/or asequence of images in the form of video to be stored in ananimated/combined video storage 2272.

Thus, as described, visual content generation system 2200 can be used togenerate video that combines live action with computer-generatedanimation using various components and tools, some of which aredescribed in more detail herein. While visual content generation system2200 might be useful for such combinations, with suitable settings, itcan be used for outputting entirely live action footage or entirely CGIsequences. The code may also be provided and/or carried by a transitorycomputer readable medium, e.g., a transmission medium such as in theform of a signal transmitted over a network.

According to one embodiment, the techniques described herein areimplemented by one or more generalized computing systems programmed toperform the techniques pursuant to program instructions in firmware,memory, other storage, or a combination. Special-purpose computingdevices may be used, such as desktop computer systems, portable computersystems, handheld devices, networking devices or any other device thatincorporates hard-wired and/or program logic to implement thetechniques.

One embodiment might include a carrier medium carrying image data orother data having details generated using the methods described herein.The carrier medium can comprise any medium suitable for carrying theimage data or other data, including a storage medium, e.g., solid-statememory, an optical disk or a magnetic disk, or a transient medium, e.g.,a signal carrying the image data such as a signal transmitted over anetwork, a digital signal, a radio frequency signal, an acoustic signal,an optical signal or an electrical signal.

Computer System

FIG. 23 is a block diagram that illustrates a computer system 2300 uponwhich the computer systems of the systems described herein and/or visualcontent generation system 2200 (see FIG. 22 ) may be implemented.Computer system 2300 includes a bus 2302 or other communicationmechanism for communicating information, and a processor 2304 coupledwith bus 2302 for processing information. Processor 2304 may be, forexample, a general-purpose microprocessor.

Computer system 2300 also includes a main memory 2306, such as arandom-access memory (RAM) or other dynamic storage device, coupled tobus 2302 for storing information and instructions to be executed byprocessor 2304. Main memory 2306 may also be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 2304. Such instructions, whenstored in non-transitory storage media accessible to processor 2304,render computer system 2300 into a special-purpose machine that iscustomized to perform the operations specified in the instructions.

Computer system 2300 further includes a read only memory (ROM) 2308 orother static storage device coupled to bus 2302 for storing staticinformation and instructions for processor 2304. A storage device 2310,such as a magnetic disk or optical disk, is provided and coupled to bus2302 for storing information and instructions.

Computer system 2300 may be coupled via bus 2302 to a display 2312, suchas a computer monitor, for displaying information to a computer user. Aninput device 2314, including alphanumeric and other keys, is coupled tobus 2302 for communicating information and command selections toprocessor 2304. Another type of user input device is a cursor control2316, such as a mouse, a trackball, or cursor direction keys forcommunicating direction information and command selections to processor2304 and for controlling cursor movement on display 2312. This inputdevice typically has two degrees of freedom in two axes, a first axis(e.g., x) and a second axis (e.g., y), that allows the device to specifypositions in a plane.

Computer system 2300 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 2300 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 2300 in response to processor 2304 executing one or moresequences of one or more instructions contained in main memory 2306.Such instructions may be read into main memory 2306 from another storagemedium, such as storage device 2310. Execution of the sequences ofinstructions contained in main memory 2306 causes processor 2304 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperation in a specific fashion. Such storage media may includenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 2310.Volatile media includes dynamic memory, such as main memory 2306. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, an EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire, and fiber optics, including thewires that include bus 2302. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 2304 for execution. Forexample, the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over anetwork connection. A modem or network interface local to computersystem 2300 can receive the data. Bus 2302 carries the data to mainmemory 2306, from which processor 2304 retrieves and executes theinstructions. The instructions received by main memory 2306 mayoptionally be stored on storage device 2310 either before or afterexecution by processor 2304.

Computer system 2300 also includes a communication interface 2318coupled to bus 2302. Communication interface 2318 provides a two-waydata communication coupling to a network link 2320 that is connected toa local network 2322. For example, communication interface 2318 may be anetwork card, a modem, a cable modem, or a satellite modem to provide adata communication connection to a corresponding type of telephone lineor communications line. Wireless links may also be implemented. In anysuch implementation, communication interface 2318 sends and receiveselectrical, electromagnetic, or optical signals that carry digital datastreams representing various types of information.

Network link 2320 typically provides data communication through one ormore networks to other data devices. For example, network link 2320 mayprovide a connection through local network 2322 to a host computer 2324or to data equipment operated by an Internet Service Provider (ISP)2326. ISP 2326 in turn provides data communication services through theworld-wide packet data communication network now commonly referred to asthe “Internet” 2328. Local network 2322 and Internet 2328 both useelectrical, electromagnetic, or optical signals that carry digital datastreams. The signals through the various networks and the signals onnetwork link 2320 and through communication interface 2318, which carrythe digital data to and from computer system 2300, are example forms oftransmission media.

Computer system 2300 can send messages and receive data, includingprogram code, through the network(s), network link 2320, andcommunication interface 2318. In the Internet example, a server 2330might transmit a requested code for an application program through theInternet 2328, ISP 2326, local network 2322, and communication interface2318. The received code may be executed by processor 2304 as it isreceived, and/or stored in storage device 2310, or other non-volatilestorage for later execution.

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. Processes described herein (or variationsand/or combinations thereof) may be performed under the control of oneor more computer systems configured with executable instructions and maybe implemented as code (e.g., executable instructions, one or morecomputer programs or one or more applications) executing collectively onone or more processors, by hardware or combinations thereof. The codemay be stored on a computer-readable storage medium, for example, in theform of a computer program comprising a plurality of instructionsexecutable by one or more processors. The computer-readable storagemedium may be non-transitory. The code may also be provided carried by atransitory computer readable medium e.g., a transmission medium such asin the form of a signal transmitted over a network. A computer-readablemedium may encompass both a non-transitory computer-readable storagemedium and a transmission medium.

Conjunctive language, such as phrases of the form “at least one of A, B,and C,” or “at least one of A, B and C,” unless specifically statedotherwise or otherwise clearly contradicted by context, is otherwiseunderstood with the context as used in general to present that an item,term, etc., may be either A or B or C, or any nonempty subset of the setof A and B and C. For instance, in the illustrative example of a sethaving three members, the conjunctive phrases “at least one of A, B, andC” and “at least one of A, B and C” refer to any of the following sets:{A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of A, at least one of B and at least one of C eachto be present.

The use of examples, or exemplary language (e.g., “such as”) providedherein, is intended merely to better illuminate embodiments of theinvention and does not pose a limitation on the scope of the inventionunless otherwise claimed. No language in the specification should beconstrued as indicating any non-claimed element as essential to thepractice of the invention.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

Further embodiments can be envisioned to one of ordinary skill in theart after reading this disclosure. In other embodiments, combinations orsub-combinations of the above-disclosed invention can be advantageouslymade. The example arrangements of components are shown for purposes ofillustration and combinations, additions, rearrangements, and the likeare contemplated in alternative embodiments of the present invention.Thus, while the invention has been described with respect to exemplaryembodiments, one skilled in the art will recognize that numerousmodifications are possible.

For example, the processes described herein may be implemented usinghardware components, software components, and/or any combinationthereof. The specification and drawings are, accordingly, to be regardedin an illustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims and that the invention is intended to cover allmodifications and equivalents within the scope of the following claims.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

Remarks

The terms “example,” “embodiment” and “implementation” are usedinterchangeably. For example, references to “one example” or “anexample” in the disclosure can be, but not necessarily are, referencesto the same implementation; and, such references mean at least one ofthe implementations. The appearances of the phrase “in one example” arenot necessarily all referring to the same example, nor are separate oralternative examples mutually exclusive of other examples. A feature,structure, or characteristic described in connection with an example canbe included in another example of the disclosure. Moreover, variousfeatures are described which can be exhibited by some examples and notby others. Similarly, various requirements are described which can berequirements for some examples but no other examples.

The terminology used herein should be interpreted in its broadestreasonable manner, even though it is being used in conjunction withcertain specific examples of the invention. The terms used in thedisclosure generally have their ordinary meanings in the relevanttechnical art, within the context of the disclosure, and in the specificcontext where each term is used. A recital of alternative language orsynonyms does not exclude the use of other synonyms. Specialsignificance should not be placed upon whether or not a term iselaborated or discussed herein. The use of highlighting has no influenceon the scope and meaning of a term. Further, it will be appreciated thatthe same thing can be said in more than one way.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import can refer to this application as a whole andnot to any particular portions of this application. Where contextpermits, words in the above Detailed Description using the singular orplural number may also include the plural or singular numberrespectively. The word “or” in reference to a list of two or more itemscovers all of the following interpretations of the word: any of theitems in the list, all of the items in the list, and any combination ofthe items in the list. The term “module” refers broadly to softwarecomponents, firmware components, and/or hardware components.

While specific examples of technology are described above forillustrative purposes, various equivalent modifications are possiblewithin the scope of the invention, as those skilled in the relevant artwill recognize. For example, while processes or blocks are presented ina given order, alternative implementations can perform routines havingsteps, or employ systems having blocks, in a different order, and someprocesses or blocks may be deleted, moved, added, subdivided, combined,and/or modified to provide alternative or sub-combinations. Each ofthese processes or blocks can be implemented in a variety of differentways. Also, while processes or blocks are at times shown as beingperformed in series, these processes or blocks can instead be performedor implemented in parallel, or can be performed at different times.Further, any specific numbers noted herein are only examples such thatalternative implementations can employ differing values or ranges.

Details of the disclosed implementations can vary considerably inspecific implementations while still being encompassed by the disclosedteachings. As noted above, particular terminology used when describingfeatures or aspects of the invention should not be taken to imply thatthe terminology is being redefined herein to be restricted to anyspecific characteristics, features, or aspects of the invention withwhich that terminology is associated. In general, the terms used in thefollowing claims should not be construed to limit the invention to thespecific examples disclosed herein, unless the above DetailedDescription explicitly defines such terms. Accordingly, the actual scopeof the invention encompasses not only the disclosed examples, but alsoall equivalent ways of practicing or implementing the invention underthe claims. Some alternative implementations can include additionalelements to those implementations described above or include fewerelements.

Any patents and applications and other references noted above, and anythat may be listed in accompanying filing papers, are incorporatedherein by reference in their entireties, except for any subject matterdisclaimers or disavowals, and except to the extent that theincorporated material is inconsistent with the express disclosureherein, in which case the language in this disclosure controls. Aspectsof the invention can be modified to employ the systems, functions, andconcepts of the various references described above to provide yetfurther implementations of the invention.

To reduce the number of claims, certain implementations are presentedbelow in certain claim forms, but the applicant contemplates variousaspects of an invention in other forms. For example, aspects of a claimcan be recited in a means-plus-function form or in other forms, such asbeing embodied in a computer-readable medium. A claim intended to beinterpreted as a means-plus-function claim will use the words “meansfor.” However, the use of the term “for” in any other context is notintended to invoke a similar interpretation. The applicant reserves theright to pursue such additional claim forms in either this applicationor in a continuing application.

1-27. (canceled)
 28. A system comprising: at least one hardwareprocessor; and at least one non-transitory memory storing instructions,which, when executed by the at least one hardware processor, cause thesystem to: obtain an image shown on a display and a pattern to detect inthe image; detect the pattern associated with the image, wherein thepattern indicates a modified region, and wherein the pattern indicatesan expected pixel value of the modified region; based on the pattern,determine the expected pixel value of the modified region; replace thepattern in the image with the expected pixel value; and store theexpected pixel value.
 29. The system of claim 28, the instructions tocalculate the expected pixel value comprising the instructions to: basedon the pattern, obtain a function to apply to the modified region; andcalculate the expected pixel value by applying the function to themodified region.
 30. The system of claim 28, the instructions tocalculate the expected pixel value comprising the instructions to:obtain a signature frequency to detect; detect the signature frequencyindicating the modified region; determine a magnitude of the signaturefrequency, wherein the magnitude of the signature frequency indicatesthe expected pixel value; and based on the magnitude of the signaturefrequency, modify a pixel value of the modified region to obtain theexpected pixel value.
 31. The system of claim 28, comprisinginstructions to: record the pattern from the display; determine asignature pixel value of the recorded pattern; and store the signaturepixel value of the recorded pattern to use in detecting the patternwithin the image presented on the display.
 32. The system of claim 28,comprising instructions to: detect a second pattern within the image,wherein the second pattern indicates a second region surrounding themodified region, wherein a pixel value of the second region has beenincreased in correspondence to a decrease in a pixel value of themodified region, wherein the second pattern indicates a second expectedpixel value associated with the second region; based on the secondpattern, calculate the second expected pixel value of the second region,wherein the second expected pixel value is lower than the pixel value ofthe second region; and replace the second pattern in the image with thesecond expected pixel value.
 33. The system of claim 28, comprising theinstructions to: train a machine learning model to detect a discrepancybetween a first image and a second image, wherein the first imageexcludes the pattern, and wherein the second image includes the firstimage modified with the pattern; and use the machine learning model todetect the pattern within the second image.
 34. The system of claim 28,the instructions to detect the pattern comprising the instructions touse a machine learning model to detect the pattern within the image. 35.The system of claim 28, comprising the instructions to: obtain an inputindicating a technique among multiple techniques, wherein the techniqueis configured to increase dynamic range of an image recorded from thedisplay; and apply at least a portion of the technique to the imagerecorded from the display.
 36. A method comprising: obtaining an imageshown on a display, and a pattern to detect in the image; obtaining thepattern associated with the image, wherein the pattern indicates amodified region, wherein the pattern indicates an expected pixel valueof the modified region; based on the pattern, determine the expectedpixel value of the modified region; replacing the pattern in the imagewith the expected pixel value; and storing the expected pixel value. 37.The method of claim 36, wherein calculating the expected pixel valuecomprises: obtaining a signature frequency to detect; detecting thesignature frequency indicating the modified region; determining amagnitude of the signature frequency, wherein the magnitude of thesignature frequency indicates the expected pixel value; and based on themagnitude of the signature frequency, modifying a pixel value of themodified region to obtain the expected pixel value.
 38. The method ofclaim 36, comprising: recording the pattern from the display;determining a signature pixel value of the recorded pattern; and storingthe signature pixel value of the recorded pattern to use in detectingthe pattern within the image presented on the display.
 39. The method ofclaim 36, comprising: detecting a second pattern within the image,wherein the second pattern indicates a second region surrounding themodified region, wherein a pixel value of the second region has beenincreased in correspondence to a decrease in a pixel value of themodified region, wherein the second pattern indicates a second expectedpixel value associated with the second region; based on the secondpattern, calculating the second expected pixel value of the secondregion, wherein the second expected pixel value is lower than the pixelvalue of the second region; and replacing the second pattern in theimage with the second expected pixel value.
 40. The method of claim 36,comprising: obtaining an input indicating a technique among multipletechniques, wherein the technique is configured to increase dynamicrange of an image recorded from the display; and applying at least aportion of the technique to the image recorded from the display.
 41. Atleast one computer-readable storage medium, excluding transitory signalsand carrying instructions, which, when executed by at least one dataprocessor of a system, causes the system to: obtain an image shown on adisplay, and a pattern to detect in the image; obtain the pattern fromthe image, wherein the pattern indicates a modified region, and whereinthe pattern indicates an expected pixel value of the modified region;based on the pattern, determine the expected pixel value of the modifiedregion; replace the pattern in the image with the expected pixel value;and store the expected pixel value.
 42. The at least onecomputer-readable storage medium of claim 41, the instructions tocalculate the expected pixel value comprising the instructions to:obtain a signature frequency to detect; detect the signature frequencyindicating the modified region; determine a magnitude of the signaturefrequency, wherein the magnitude of the signature frequency indicatesthe expected pixel value; and based on the magnitude of the signaturefrequency, modify a pixel value of the modified region to obtain theexpected pixel value.
 43. The at least one computer-readable storagemedium of claim 41, comprising instructions to: record the pattern fromthe display; determine a signature pixel value of the recorded pattern;and store the signature pixel value of the recorded pattern to use indetecting the pattern within the image presented on the display.
 44. Theat least one computer-readable storage medium of claim 41, comprisinginstructions to: detect a second pattern within the image, wherein thesecond pattern indicates a second region surrounding the modifiedregion, wherein a pixel value of the second region has been increased incorrespondence to a decrease in a pixel value of the modified region,wherein the second pattern indicates a second expected pixel valueassociated with the second region; based on the second pattern,calculate the second expected pixel value of the second region, whereinthe second expected pixel value is lower than the pixel value of thesecond region; and replace the second pattern in the image with thesecond expected pixel value.
 45. The at least one computer-readablestorage medium of claim 41, comprising the instructions to: train amachine learning model to detect a discrepancy between a first image anda second image, wherein the first image excludes the pattern, andwherein the second image includes the first image modified with thepattern; and use the machine learning model to detect the pattern withinthe second image.
 46. The at least one computer-readable storage mediumof claim 41, the instructions to detect the pattern comprising theinstructions to use a Fast Fourier Transform to detect the patternwithin the image.
 47. The at least one computer-readable storage mediumof claim 41, comprising the instructions to: obtain an input indicatinga technique among multiple techniques, wherein the technique isconfigured to increase dynamic range of an image recorded from thedisplay; and apply at least a portion of the technique to the imagerecorded from the display.